research papers on banking and finance

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• Published: 18 June 2021

Financial technology and the future of banking

• Daniel Broby   ORCID: orcid.org/0000-0001-5482-0766 1  

Financial Innovation volume  7 , Article number:  47 ( 2021 ) Cite this article

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This paper presents an analytical framework that describes the business model of banks. It draws on the classical theory of banking and the literature on digital transformation. It provides an explanation for existing trends and, by extending the theory of the banking firm, it illustrates how financial intermediation will be impacted by innovative financial technology applications. It further reviews the options that established banks will have to consider in order to mitigate the threat to their profitability. Deposit taking and lending are considered in the context of the challenge made from shadow banking and the all-digital banks. The paper contributes to an understanding of the future of banking, providing a framework for scholarly empirical investigation. In the discussion, four possible strategies are proposed for market participants, (1) customer retention, (2) customer acquisition, (3) banking as a service and (4) social media payment platforms. It is concluded that, in an increasingly digital world, trust will remain at the core of banking. That said, liquidity transformation will still have an important role to play. The nature of banking and financial services, however, will change dramatically.

Introduction

The bank of the future will have several different manifestations. This paper extends theory to explain the impact of financial technology and the Internet on the nature of banking. It provides an analytical framework for academic investigation, highlighting the trends that are shaping scholarly research into these dynamics. To do this, it re-examines the nature of financial intermediation and transactions. It explains how digital banking will be structurally, as well as physically, different from the banks described in the literature to date. It does this by extending the contribution of Klein ( 1971 ), on the theory of the banking firm. It presents suggested strategies for incumbent, and challenger banks, and how banking as a service and social media payment will reshape the competitive landscape.

The banking industry has been evolving since Banca Monte dei Paschi di Siena opened its doors in 1472. Its leveraged business model has proved very scalable over time, but it is now facing new challenges. Firstly, its book to capital ratios, as documented by Berger et al ( 1995 ), have been consistently falling since 1840. This trend continues as competition has increased. In the past decade, the industry has experienced declines in profitability as measured by return on tangible equity. This is partly the result of falling leverage and fee income and partly due to the net interest margin (connected to traditional lending activity). These trends accelerated following the 2008 financial crisis. At the same time, technology has made banks more competitive. Advances in digital technology are changing the very nature of banking. Banks are now distributing services via mobile technology. A prolonged period of very low interest rates is also having an impact. To sustain their profitability, Brei et al. ( 2020 ) note that many banks have increased their emphasis on fee-generating services.

As Fama ( 1980 ) explains, a bank is an intermediary. The Internet is, however, changing the way financial service providers conduct their role. It is fundamentally changing the nature of the banking. This in turn is changing the nature of banking services, and the way those services are delivered. As a consequence, in order to compete in the changing digital landscape, banks have to adapt. The banks of the future, both incumbents and challengers, need to address liquidity transformation, data, trust, competition, and the digitalization of financial services. Against this backdrop, incumbent banks are focused on reinventing themselves. The challenger banks are, however, starting with a blank canvas. The research questions that these dynamics pose need to be investigated within the context of the theory of banking, hence the need to revise the existing analytical framework.

Banks perform payment and transfer functions for an economy. The Internet can now facilitate and even perform these functions. It is changing the way that transactions are recorded on ledgers and is facilitating both public and private digital currencies. In the past, banks operated in a world of information asymmetry between themselves and their borrowers (clients), but this is changing. This differential gave one bank an advantage over another due to its knowledge about its clients. The digital transformation that financial technology brings reduces this advantage, as this information can be digitally analyzed.

Even the nature of deposits is being transformed. Banks in the future will have to accept deposits and process transactions made in digital form, either Central Bank Digital Currencies (CBDC) or cryptocurrencies. This presents a number of issues: (1) it changes the way financial services will be delivered, (2) it requires a discussion on resilience, security and competition in payments, (3) it provides a building block for better cross border money transfers and (4) it raises the question of private and public issuance of money. Braggion et al ( 2018 ) consider whether these represent a threat to financial stability.

The academic study of banking began with Edgeworth ( 1888 ). He postulated that it is based on probability. In this respect, the nature of the business model depends on the probability that a bank will not be called upon to meet all its liabilities at the same time. This allows banks to lend more than they have in deposits. Because of the resultant mismatch between long term assets and short-term liabilities, a bank’s capital structure is very sensitive to liquidity trade-offs. This is explained by Diamond and Rajan ( 2000 ). They explain that this makes a bank a’relationship lender’. In effect, they suggest a bank is an intermediary that has borrowed from other investors.

Diamond and Rajan ( 2000 ) argue a lender can negotiate repayment obligations and that a bank benefits from its knowledge of the customer. As shall be shown, the new generation of digital challenger banks do not have the same tradeoffs or knowledge of the customer. They operate more like a broker providing a platform for banking services. This suggests that there will be more than one type of bank in the future and several different payment protocols. It also suggests that banks will have to data mine customer information to improve their understanding of a client’s financial needs.

The key focus of Diamond and Rajan ( 2000 ), however, was to position a traditional bank is an intermediary. Gurley and Shaw ( 1956 ) describe how the customer relationship means a bank can borrow funds by way of deposits (liabilities) and subsequently use them to lend or invest (assets). In facilitating this mediation, they provide a service whereby they store money and provide a mechanism to transmit money. With improvements in financial technology, however, money can be stored digitally, lenders and investors can source funds directly over the internet, and money transfer can be done digitally.

A review of financial technology and banking literature is provided by Thakor ( 2020 ). He highlights that financial service companies are now being provided by non-deposit taking contenders. This paper addresses one of the four research questions raised by his review, namely how theories of financial intermediation can be modified to accommodate banks, shadow banks, and non-intermediated solutions.

To be a bank, an entity must be authorized to accept retail deposits. A challenger bank is, therefore, still a bank in the traditional sense. It does not, however, have the costs of a branch network. A peer-to-peer lender, meanwhile, does not have a deposit base and therefore acts more like a broker. This leads to the issue that this paper addresses, namely how the banks of the future will conduct their intermediation.

In order to understand what the bank of the future will look like, it is necessary to understand the nature of the aforementioned intermediation, and the way it is changing. In this respect, there are two key types of intermediation. These are (1) quantitative asset transformation and, (2) brokerage. The latter is a common model adopted by challenger banks. Figure  1 depicts how these two types of financial intermediation match savers with borrowers. To avoid nuanced distinction between these two types of intermediation, it is common to classify banks by the services they perform. These can be grouped as either private, investment, or commercial banking. The service sub-groupings include payments, settlements, fund management, trading, treasury management, brokerage, and other agency services.

How banks act as intermediaries between lenders and borrowers. This function call also be conducted by intermediaries as brokers, for example by shadow banks. Disintermediation occurs over the internet where peer-to-peer lenders match savers to lenders

Financial technology has the ability to disintermediate the banking sector. The competitive pressures this results in will shape the banks of the future. The channels that will facilitate this are shown in Fig.  2 , namely the Internet and/or mobile devices. Challengers can participate in this by, (1) directly matching borrows with savers over the Internet and, (2) distributing white labels products. The later enables banking as a service and avoids the aforementioned liquidity mismatch.

The strategic options banks have to match lenders with borrowers. The traditional and challenger banks are in the same space, competing for business. The distributed banks use the traditional and challenger banks to white label banking services. These banks compete with payment platforms on social media. The Internet heralds an era of banking as a service

There are also physical changes that are being made in the delivery of services. Bricks and mortar branches are in decline. Mobile banking, or m-banking as Liu et al ( 2020 ) describe it, is an increasingly important distribution channel. Robotics are increasingly being used to automate customer interaction. As explained by Vishnu et al ( 2017 ), these improve efficiency and the quality of execution. They allow for increased oversight and can be built on legacy systems as well as from a blank canvas. Application programming interfaces (APIs) are bringing the same type of functionality to m-banking. They can be used to authorize third party use of banking data. How banks evolve over time is important because, according to the OECD, the activity in the financial sector represents between 20 and 30 percent of developed countries Gross Domestic Product.

In summary, financial technology has evolved to a level where online banks and banking as a service are challenging incumbents and the nature of banking mediation. Banking is rapidly transforming because of changes in such technology. At the same time, the solving of the double spending problem, whereby digital money can be cryptographically protected, has led to the possibility that paper money will become redundant at some point in the future. A theoretical framework is required to understand this evolving landscape. This is discussed next.

The theory of the banking firm: a revision

In financial theory, as eloquently explained by Fama ( 1980 ), banking provides an accounting system for transactions and a portfolio system for the storage of assets. That will not change for the banks of the future. Fama ( 1980 ) explains that their activities, in an unregulated state, fulfil the Modigliani–Miller ( 1959 ) theorem of the irrelevance of the financing decision. In practice, traditional banks compete for deposits through the interest rate they offer. This makes the transactional element dependent on the resulting debits and credits that they process, essentially making banks into bookkeeping entities fulfilling the intermediation function. Since this is done in response to competitive forces, the general equilibrium is a passive one. As such, the banking business model is vulnerable to disruption, particularly by innovation in financial technology.

A bank is an idiosyncratic corporate entity due to its ability to generate credit by leveraging its balance sheet. That balance sheet has assets on one side and liabilities on the other, like any corporate entity. The assets consist of cash, lending, financial and fixed assets. On the other side of the balance sheet are its liabilities, deposits, and debt. In this respect, a bank’s equity and its liabilities are its source of funds, and its assets are its use of funds. This is explained by Klein ( 1971 ), who notes that a bank’s equity W , borrowed funds and its deposits B is equal to its total funds F . This is the same for incumbents and challengers. This can be depicted algebraically if we let incumbents be represented by Φ and challengers represented by Γ:

Klein ( 1971 ) further explains that a bank’s equity is therefore made up of its share capital and unimpaired reserves. The latter are held by a bank to protect the bank’s deposit clients. This part is also mandated by regulation, so as to protect customers and indeed the entire banking system from systemic failure. These protective measures include other prudential requirements to hold cash reserves or other liquid assets. As shall be shown, banking services can be performed over the Internet without these protections. Banking as a service, as this phenomenon known, is expected to increase in the future. This will change the nature of the protection available to clients. It will change the way banks transform assets, explained next.

A bank’s deposits are said to be a function of the proportion of total funds obtained through the issuance of the ith deposit type and its total funds F , represented by α i . Where deposits, represented by Bs , are made in the form of Bs (i  =  1 *s n) , they generate a rate of interest. It follows that Si Bs  =  B . As such,

Therefor it can be said that,

The importance of Eq. 3 is that the balance sheet can be leveraged by the issuance of loans. It should be noted, however, that not all loans are returned to the bank in whole or part. Non-performing loans reduce the asset side of a bank’s balance sheet and act as a constraint on capital, and therefore new lending. Clearly, this is not the case with banking as a service. In that model, loans are brokered. That said, with the traditional model, an advantage of financial technology is that it facilitates the data mining of clients’ accounts. Lending can therefore be more targeted to borrowers that are more likely to repay, thereby reducing non-performing loans. Pari passu, the incumbent bank of the future will therefore have a higher risk-adjusted return on capital. In practice, however, banking as a service will bring greater competition from challengers and possible further erosion of margins. Alternatively, some banks will proactively engage in partnerships and acquisitions to maintain their customer base and address the competition.

A bank must have reserves to meet the demand of customers demanding their deposits back. The amount of these reserves is a key function of banking regulation. The Basel Committee on Banking Supervision mandates a requirement to hold various tiers of capital, so that banks have sufficient reserves to protect depositors. The Committee also imposes a framework for mitigating excessive liquidity risk and maturity transformation, through a set Liquidity Coverage Ratio and Net Stable Funding Ratio.

Recent revisions of theory, because of financial technology advances, have altered our understanding of banking intermediation. This will impact the competitive landscape and therefor shape the nature of the bank of the future. In this respect, the threat to incumbent banks comes from peer-to-peer Internet lending platforms. These perform the brokerage function of financial intermediation without the use of the aforementioned banking balance sheet. Unlike regulated deposit takers, such lending platforms do not create assets and do not perform risk and asset transformation. That said, they are reliant on investors who do not always behave in a counter cyclical way.

Financial technology in banking is not new. It has been used to facilitate electronic markets since the 1980’s. Thakor ( 2020 ) refers to three waves of application of financial innovation in banking. The advent of institutional futures markets and the changing nature of financial contracts fundamentally changed the role of banks. In response to this, academics extended the concept of a bank into an entity that either fulfills the aforementioned functions of a broker or a qualitative asset transformer. In this respect, they connect the providers and users of capital without changing the nature of the transformation of the various claims to that capital. This transformation can be in the form risk transfer or the application of leverage. The nature of trading of financial assets, however, is changing. Price discovery can now be done over the Internet and that is moving liquidity from central marketplaces (like the stock exchange) to decentralized ones.

Alongside these trends, in considering what the bank of the future will look like, it is necessary to understand the unregulated lending market that competes with traditional banks. In this part of the lending market, there has been a rise in shadow banks. The literature on these entities is covered by Adrian and Ashcraft ( 2016 ). Shadow banks have taken substantial market share from the traditional banks. They fulfil the brokerage function of banks, but regulators have only partial oversight of their risk transformation or leverage. The rise of shadow banks has been facilitated by financial technology and the originate to distribute model documented by Bord and Santos ( 2012 ). They use alternative trading systems that function as electronic communication networks. These facilitate dark pools of liquidity whereby buyers and sellers of bonds and securities trade off-exchange. Since the credit crisis of 2008, total broker dealer assets have diverged from banking assets. This illustrates the changed lending environment.

In the disintermediated market, banking as a service providers must rely on their equity and what access to funding they can attract from their online network. Without this they are unable to drive lending growth. To explain this, let I represent the online network. Extending Klein ( 1971 ), further let Ψ represent banking as a service and their total funds by F . This state is depicted as,

Theoretically, it can be shown that,

Shadow banks, and those disintermediators who bypass the banking system, have an advantage in a world where technology is ubiquitous. This becomes more apparent when costs are considered. Buchak et al. ( 2018 ) point out that shadow banks finance their originations almost entirely through securitization and what they term the originate to distribute business model. Diversifying risk in this way is good for individual banks, as banking risks can be transferred away from traditional banking balance sheets to institutional balance sheets. That said, the rise of securitization has introduced systemic risk into the banking sector.

Thus, we can see that the nature of banking capital is changing and at the same time technology is replacing labor. Let A denote the number of transactions per account at a period in time, and C denote the total cost per account per time period of providing the services of the payment mechanism. Klein ( 1971 ) points out that, if capital and labor are assumed to be part of the traditional banking model, it can be observed that,

It can therefore be observed that the total service charge per account at a period in time, represented by S, has a linear and proportional relationship to bank account activity. This is another variable that financial technology can impact. According to Klein ( 1971 ) this can be summed up in the following way,

where d is the basic bank decision variable, the service charge per transaction. Once again, in an automated and digital environment, financial technology greatly reduces d for the challenger banks. Swankie and Broby ( 2019 ) examine the impact of Artificial Intelligence on the evaluation of banking risk and conclude that it improves such variables.

Meanwhile, the traditional banking model can be expressed as a product of the number of accounts, M , and the average size of an account, N . This suggests a banks implicit yield is it rate of interest on deposits adjusted by its operating loss in each time period. This yield is generated by payment and loan services. Let R 1 depict this. These can be expressed as a fraction of total demand deposits. This is depicted by Klein ( 1971 ), if one assumes activity per account is constant, as,

As a result, whether a bank is structured with traditional labor overheads or built digitally, is extremely relevant to its profitability. The capital and labor of tradition banks, depicted as Φ i , is greater than online networks, depicted as I i . As such, the later have an advantage. This can be shown as,

What Klein (1972) failed to highlight is that the banking inherently involves leverage. Diamond and Dybving (1983) show that leverage makes bank susceptible to run on their liquidity. The literature divides these between adverse shock events, as explained by Bernanke et al ( 1996 ) or moral hazard events as explained by Demirgu¨¸c-Kunt and Detragiache ( 2002 ). This leverage builds on the balance sheet mismatch of short-term assets with long term liabilities. As such, capital and liquidity are intrinsically linked to viability and solvency.

The way capital and liquidity are managed is through credit and default management. This is done at a bank level and a supervisory level. The Basel Committee on Banking Supervision applies capital and leverage ratios, and central banks manage interest rates and other counter-cyclical measures. The various iterations of the prudential regulation of banks have moved the microeconomic theory of banking from the modeling of risk to the modeling of imperfect information. As mentioned, shadow and disintermediated services do not fall under this form or prudential regulation.

The relationship between leverage and insolvency risk crucially depends on the degree of banks total funds F and their liability structure L . In this respect, the liability structure of traditional banks is also greater than online networks which do not have the same level of available funds, depicted as,

Diamond and Dybvig ( 1983 ) observe that this liability structure is intimately tied to a traditional bank’s assets. In this respect, a bank’s ability to finance its lending at low cost and its ability to achieve repayment are key to its avoidance of insolvency. Online networks and/or brokers do not have to finance their lending, simply source it. Similarly, as brokers they do not face capital loss in the event of a default. This disintermediates the bank through the use of a peer-to-peer environment. These lenders and borrowers are introduced in digital way over the internet. Regulators have taken notice and the digital broker advantage might not last forever. As a result, the future may well see greater cooperation between these competing parties. This also because banks have valuable operational experience compared to new entrants.

It should also be observed that bank lending is either secured or unsecured. Interest on an unsecured loan is typically higher than the interest on a secured loan. In this respect, incumbent banks have an advantage as their closeness to the customer allows them to better understand the security of the assets. Berger et al ( 2005 ) further differentiate lending into transaction lending, relationship lending and credit scoring.

The evolution of the business model in a digital world

As has been demonstrated, the bank of the future in its various manifestations will be a consequence of the evolution of the current banking business model. There has been considerable scholarly investigation into the uniqueness of this business model, but less so on its changing nature. Song and Thakor ( 2010 ) are helpful in this respect and suggest that there are three aspects to this evolution, namely competition, complementary and co-evolution. Although liquidity transformation is evolving, it remains central to a bank’s role.

All the dynamics mentioned are relevant to the economy. There is considerable evidence, as outlined by Levine ( 2001 ), that market liberalization has a causal impact on economic growth. The impact of technology on productivity should prove positive and enhance the functioning of the domestic financial system. Indeed, market liberalization has already reshaped banking by increasing competition. New fee based ancillary financial services have become widespread, as has the proprietorial use of balance sheets. Risk has been securitized and even packaged into trade-able products.

Challenger banks are developing in a complementary way with the incumbents. The latter have an advantage over new entrants because they have information on their customers. The liquidity insurance model, proposed by Diamond and Dybvig ( 1983 ), explains how such banks have informational advantages over exchange markets. That said, financial technology changes these dynamics. It if facilitating the processing of financial data by third parties, explained in greater detail in the section on Open Banking.

At the same time, financial technology is facilitating banking as a service. This is where financial services are delivered by a broker over the Internet without resort to the balance sheet. This includes roboadvisory asset management, peer to peer lending, and crowd funding. Its growth will be facilitated by Open Banking as it becomes more geographically adopted. Figure  3 illustrates how these business models are disintermediating the traditional banking role and matching burrowers and savers.

The traditional view of banks ecosystem between savers and borrowers, atop the Internet which is matching savers and borrowers directly in a peer-to-peer way. The Klein ( 1971 ) theory of the banking firm does not incorporate the mirrored dynamics, and as such needs to be extended to reflect the digital innovation that impacts both borrowers and severs in a peer-to-peer environment

Meanwhile, the banking sector is co-evolving alongside a shadow banking phenomenon. Lenders and borrowers are interacting, but outside of the banking sector. This is a concern for central banks and banking regulators, as the lending is taking place in an unregulated environment. Shadow banking has grown because of financial technology, market liberalization and excess liquidity in the asset management ecosystem. Pozsar and Singh ( 2011 ) detail the non-bank/bank intersection of shadow banking. They point out that shadow banking results in reverse maturity transformation. Incumbent banks have blurred the distinction between their use of traditional (M2) liabilities and market-based shadow banking (non-M2) liabilities. This impacts the inter-generational transfers that enable a bank to achieve interest rate smoothing.

Securitization has transformed the risk in the banking sector, transferring it to asset management institutions. These include structured investment vehicles, securities lenders, asset backed commercial paper investors, credit focused hedge and money market funds. This in turn has led to greater systemic risk, the result of the nature of the non-traded liabilities of securitized pooling arrangements. This increased risk manifested itself in the 2008 credit crisis.

Commercial pressures are also shaping the banking industry. The drive for cost efficiency has made incumbent banks address their personally costs. Bank branches have been closed as technology has evolved. Branches make it easier to withdraw or transfer deposits and challenger banks are not as easily able to attract new deposits. The banking sector is therefore looking for new point of customer contact, such as supermarkets, post offices and social media platforms. These structural issues are occurring at the same time as the retail high street is also evolving. Banks have had an aggressive roll out of automated telling machines and a reduction in branches and headcount. Online digital transactions have now become the norm in most developed countries.

The financing of banks is also evolving. Traditional banks have tended to fund illiquid assets with short term and unstable liquid liabilities. This is one of the key contributors to the rise to the credit crisis of 2008. The provision of liquidity as a last resort is central to the asset transformation process. In this respect, the banking sector experienced a shock in 2008 in what is termed the credit crisis. The aforementioned liquidity mismatch resulted in the system not being able to absorb all the risks associated with subprime lending. Central banks had to resort to quantitative easing as a result of the failure of overnight funding mechanisms. The image of the entire banking sector was tarnished, and the banks of the future will have to address this.

The future must learn from the mistakes of the past. The structural weakness of the banking business model cannot be solved. That said, the latest Basel rules introduce further risk mitigation, improved leverage ratios and increased levels of capital reserve. Another lesson of the credit crisis was that there should be greater emphasis on risk culture, governance, and oversight. The independence and performance of the board, the experience and the skill set of senior management are now a greater focus of regulators. Internal controls and data analysis are increasingly more robust and efficient, with a greater focus on a banks stable funding ratio.

Meanwhile, the very nature of money is changing. A digital wallet for crypto-currencies fulfills much the same storage and transmission functions of a bank; and crypto-currencies are increasing being used for payment. Meanwhile, in Sweden, stores have the right to refuse cash and the majority of transactions are card based. This move to credit and debit cards, and the solving of the double spending problem, whereby digital money can be crypto-graphically protected, has led to the possibility that paper money could be replaced at some point in the future. Whether this might be by replacement by a CBDC, or decentralized digital offering, is of secondary importance to the requirement of banks to adapt. Whether accommodating crytpo-currencies or CBDC’s, Kou et al. ( 2021 ) recommend that banks keep focused on alternative payment and money transferring technologies.

Central banks also have to adapt. To limit disintermediation, they have to ensure that the economic design of their sponsored digital currencies focus on access for banks, interest payment relative to bank policy rate, banking holding limits and convertibility with bank deposits. All these developments have implications for banks, particularly in respect of funding, the secure storage of deposits and how digital currency interacts with traditional fiat money.

Open banking

Against the backdrop of all these trends and changes, a new dynamic is shaping the future of the banking sector. This is termed Open Banking, already briefly mentioned. This new way of handling banking data protocols introduces a secure way to give financial service companies consensual access to a bank’s customer financial information. Figure  4 illustrates how this works. Although a fairly simple concept, the implications are important for the banking industry. Essentially, a bank customer gives a regulated API permission to securely access his/her banking website. That is then used by a banking as a service entity to make direct payments and/or download financial data in order to provide a solution. It heralds an era of customer centric banking.

How Open Banking operates. The customer generates data by using his bank account. A third party provider is authorized to access that data through an API request. The bank confirms digitally that the customer has authorized the exchange of data and then fulfills the request

Open Banking was a response to the documented inertia around individual’s willingness to change bank accounts. Following the Retail Banking Review in the UK, this was addressed by lawmakers through the European Union’s Payment Services Directive II. The legislation was designed to make it easier to change banks by allowing customers to delegate authority to transfer their financial data to other parties. As a result of this, a whole host of data centric applications were conceived. Open banking adds further momentum to reshaping the future of banking.

Open Banking has a number of quite revolutionary implications. It was started so customers could change banks easily, but it resulted in some secondary considerations which are going to change the future of banking itself. It gives a clear view of bank financing. It allows aggregation of finances in one place. It also allows can give access to attractive offerings by allowing price comparisons. Open Banking API’s build a secure online financial marketplace based on data. They also allow access to a larger market in a faster way but the third-party providers for the new entrants. Open Banking allows developers to build single solutions on an API addressing very specific problems, like for example, a cash flow based credit rating.

Romānova et al. ( 2018 ) undertook a questionnaire on the Payment Services Directive II. The results suggest that Open Banking will promote competitiveness, innovation, and new product development. The initiative is associated with low costs and customer satisfaction, but that some concerns about security, privacy and risk are present. These can be mitigated, to some extent, by secure protocols and layered permission access.

Discussion: strategic options

Faced with these disruptive trends, there are four strategic options for market participants to con- sider. There are (1) a defensive customer retention strategy for incumbents, (2) an aggressive customer acquisition strategy for challenger banks (3) a banking as a service strategy for new entrants, and (4) a payments strategy for social media platforms.

Each of these strategies has to be conducted in a competitive marketplace for money demand by potential customers. Figure  5 illustrates where the first three strategies lie on the tradeoff between money demand and interest rates. The payment strategy can’t be modeled based on the supply of money. In the figure, the market settles at a rate L 2 . The incumbent banks have the capacity to meet the largest supply of these loans. The challenger banks have a constrained function but due to a lower cost base can gain excess rent through higher rates of interest. The peer-to-peer bank as a service brokers must settle for the market rate and a constrained supply offering.

The money demand M by lenders on the y axis. Interest rates on the y axis are labeled as r I and r II . The challenger banks are represented by the line labeled Γ. They have a price and technology advantage and so can lend at higher interest rates. The brokers are represented by the line labeled Ω. They are price takers, accepting the interest rate determined by the market. The same is true for the incumbents, represented by the line labeled Φ but they have a greater market share due to their customer relationships. Note that payments strategy for social media platforms is not shown on this figure as it is not affected by interest rates

Figure  5 illustrates that having a niche strategy is not counterproductive. Liu et al ( 2020 ) found that banks performing niche activities exhibit higher profitability and have lower risk. The syndication market now means that a bank making a loan does not have to be the entity that services it. This means banks in the future can better shape their risk profile and manage their lending books accordingly.

An interesting question for central banks is what the future Deposit Supply function will look like. If all three forms: open banking, traditional banking and challenger banks develop together, will the bank of the future have the same Deposit Supply function? The Klein ( 1971 ) general formulation assumes that deposits are increasing functions of implicit and explicit yields. As such, the very nature of central bank directed monetary policy may have to be revisited, as alluded to in the earlier discussion on digital money.

The client retention strategy (incumbents)

The competitive pressures suggest that incumbent banks need to focus on customer retention. Reichheld and Kenny ( 1990 ) found that the best way to do this was to focus on the retention of branch deposit customers. Obviously, another way is to provide a unique digital experience that matches the challengers.

Incumbent banks have a competitive advantage based on the information they have about their customers. Allen ( 1990 ) argues that where risk aversion is observable, information markets are viable. In other words, both bank and customer benefit from this. The strategic issue for them, therefore, becomes the retention of these customers when faced with greater competition.

Open Banking changes the dynamics of the banking information advantage. Borgogno and Colangelo ( 2020 ) suggest that the access to account (XS2A) rule that it introduced will increase competition and reduce information asymmetry. XS2A requires banks to grant access to bank account data to authorized third payment service providers.

The incumbent banks have a high-cost base and legacy IT systems. This makes it harder for them to migrate to a digital world. There are, however, also benefits from financial technology for the incumbents. These include reduced cost and greater efficiency. Financial technology can also now support platforms that allow incumbent banks to sell NPL’s. These platforms do not require the ownership of assets, they act as consolidators. The use of technology to monitor the transactions make the processing cost efficient. The unique selling point of such platforms is their centralized point of contact which results in a reduction in information asymmetry.

Incumbent banks must adapt a number of areas they got to adapt in terms of their liquidity transformation. They have to adapt the way they handle data. They must get customers to trust them in a digital world and the way that they trust them in a bricks and mortar world. It is no coincidence. When you go into a bank branch that is a great big solid building great big facade and so forth that is done deliberately so that you trust that bank with your deposit.

The risk of having rising non-performing loans needs to be managed, so customer retention should be selective. One of the puzzles in banking is why customers are regularly denied credit, rather than simply being charged a higher price for it. This credit rationing is often alleviated by collateral, but finance theory suggests value is based on the discounted sum of future cash flows. As such, it is conceivable that the bank of the future will use financial technology to provide innovative credit allocation solutions. That said, the dual risks of moral hazard and information asymmetries from the adoption of such solutions must be addressed.

Customer retention is especially important as bank competition is intensifying, as is the digitalization of financial services. Customer retention requires innovation, and that innovation has been moving at a very fast rate. Until now, banks have traditionally been hesitant about technology. More recently, mergers and acquisitions have increased quite substantially, initiated by a need to address actual or perceived weaknesses in financial technology.

The client acquisition strategy (challengers)

As intermediaries, the challenger banks are the same as incumbent banks, but designed from the outset to be digital. This gives them a cost and efficiency advantage. Anagnostopoulos ( 2018 ) suggests that the difference between challenger and traditional banks is that the former address its customers problems more directly. The challenge for such banks is customer acquisition.

Open Banking is a major advantage to challenger banks as it facilitates the changing of accounts. There is widespread dissatisfaction with many incumbent banks. Open Banking makes it easier to change accounts and also easier to get a transaction history on the client.

Customer acquisition can be improved by building trust in a brand. Historically, a bank was physically built in a very robust manner, hence the heavy architecture and grand banking halls. This was done deliberately to engender a sense of confidence in the deposit taking institution. Pure internet banks are not able to do this. As such, they must employ different strategies to convey stability. To do this, some communicate their sustainability credentials, whilst others use generational values-based advertising. Customer acquisition in a banking context is traditionally done by offering more attractive rates of interest. This is illustrated in Fig.  5 by the intersect of traditional banks with the market rate of interest, depicted where the line Γ crosses L 2 . As a result of the relationship with banking yield, teaser rates and introductory rates are common. A customer acquisition strategy has risks, as consumers with good credit can game different challenger banks by frequently changing accounts.

Most customer acquisition, however, is done based on superior service offering. The functionality of challenger banking accounts is often superior to incumbents, largely because the latter are built on legacy databases that have inter-operability issues. Having an open platform of services is a popular customer acquisition technique. The unrestricted provision of third-party products is viewed more favorably than a restricted range of products.

The banking as a service strategy (new entrants)

Banking from a customer’s perspective is the provision of a service. Customers don’t care about the maturity transformation of banking balance sheets. Banking as a service can be performed without recourse to these balance sheets. Banking products are brokered, mostly by new entrants, to individuals as services that can be subscribed to or paid on a fee basis.

There are a number banking as a service solutions including pre-paid and credit cards, lending and leasing. The banking as a service brokers are effectively those that are aggregating services from others using open banking to enable banking as a service.

The rise of banking as a service needs to be understood as these compete directly with traditional banks. As explained, some of these do this through peer-to-peer lending over the internet, others by matching borrows and sellers, conducting mediation as a loan broker. Such entities do not transform assets and do not have banking licenses. They do not have a branch network and often don not have access to deposits. This means that they have no insurance protection and can be subject to interest rate controls.

The new genre of financial technology, banking as a service provider, conduct financial services transformation without access to central bank liquidity. In a distributed digital asset world, the assets are stored on a distributed ledger rather than a traditional banking ledger. Financial technology has automated credit evaluation, savings, investments, insurance, trading, banking payments and risk management. These banking as a service offering are only as secure as the technology on which they are built.

The social media payment strategy (disintermediators and disruptors)

An intermediation bank is a conceptual idea, one created solely on a social networking site. Social media has developed a market for online goods and services. Williams ( 2018 ) estimates that there are 2.46 billion social media users. These all make and receive payments of some kind. They demand security and functionality. Importantly, they have often more clients than most banks. As such, a strategy to monetize the payments infrastructure makes sense.

All social media platforms are rich repositories of data. Such platforms are used to buy and sell things and that requires payments. Some platforms are considering evolving their own digital payment, cutting out the banks as middlemen. These include Facebook’s Diem (formerly Libra), a digital currency, and similar developments at some of the biggest technology companies. The risk with social media payment platform is that there is systemic counter-party protection. Regulators need to address this. One way to do this would be to extend payment service insurance to such platforms.

Social media as a platform moves the payment relationship from a transaction to a customer experience. The ability to use consumer desires in combination with financial data has the potential to deliver a number of new revenue opportunities. These will compete directly with the banks of the future. This will have implications for (1) the money supply, (2) the market share of traditional banks and, (3) the services that payment providers offer.

Further research

Several recommendations for research derive from both the impact of disintermediation and the four proposed strategies that will shape banking in the future. The recommendations and suggestions are based on the mentioned papers and the conclusions drawn from them.

As discussed, the nature of intermediation is changing, and this has implications for the pricing of risk. The role of interest rates in banking will have to be further reviewed. In a decentralized world based on crypto currencies the central banks do not have the same control over the money supply, This suggest the quantity theory of money and the liquidity preference theory need to be revisited. As explained, the Internet reduces much of the friction costs of intermediation. Researchers should ask how this will impact maturity transformation. It is also fair to ask whether at some point in the future there will just be one big bank. This question has already been addressed in the literature but the Internet facilities the possibility. Diamond ( 1984 ) and Ramakrishnan and Thakor ( 1984 ) suggested the answer was due to diversification and its impact on reducing monitoring costs.

Attention should be given by academics to the changing nature of banking risk. How should regulators, for example, address the moral hazard posed by challenger banks with weak balance sheets? What about deposit insurance? Should it be priced to include unregulated entities? Also, what criteria do borrowers use to choose non-banking intermediaries? The changing risk environment also poses two interesting practical questions. What will an online bank run look like, and how can it be averted? How can you establish trust in digital services?

There are also research questions related to the nature of competition. What, for example, will be the nature of cross border competition in a decentralized world? Is the credit rationing that generates competition a static or dynamic phenomena online? What is the value of combining consumer utility with banking services?

Financial intermediaries, like banks, thrive in a world of deficits and surpluses supported by information asymmetries and disconnectedness. The connectivity of the internet changes this dynamic. In this respect, the view of Schumpeter ( 1911 ) on the role of financial intermediaries needs revisiting. Lenders and borrows can be connected peer to peer via the internet.

All the dynamics mentioned change the nature of moral hazard. This needs further investigation. There has been much scholarly research on the intrinsic riskiness of the mismatch between banking assets and liabilities. This mismatch not only results in potential insolvency for a single bank but potentially for the whole system. There has, for example, been much debate on the whether a bank can be too big to fail. As a result of the riskiness of the banking model, the banks of the future will be just a liable to fail as the banks of the past.

This paper presented a revision of the theory of banking in a digital world. In this respect, it built on the work of Klein ( 1971 ). It provided an overview of the changing nature of banking intermediation, a result of the Internet and new digital business models. It presented the traditional academic view of banking and how it is evolving. It showed how this is adapted to explain digital driven disintermediation.

It was shown that the banking industry is facing several documented challenges. Risk is being taken of balance sheet, securitized, and brokered. Financial technology is digitalizing service delivery. At the same time, the very nature of intermediation is being changed due to digital currency. It is argued that the bank of the future not only has to face these competitive issues, but that technology will enhance the delivery of banking services and reduce the cost of their delivery.

The paper further presented the importance of the Open Banking revolution and how that facilitates banking as a service. Open Banking is increasing client churn and driving banking as a service. That in turn is changing the way products are delivered.

Four strategies were proposed to navigate the evolving competitive landscape. These are for incumbents to address customer retention; for challengers to peruse a low-cost digital experience; for niche players to provide banking as a service; and for social media platforms to develop payment platforms. In all these scenarios, the banks of the future will have to have digital strategies for both payments and service delivery.

It was shown that both incumbents and challengers are dependent on capital availability and borrowers credit concerns. Nothing has changed in that respect. The risks remain credit and default risk. What is clear, however, is the bank has become intrinsically linked with technology. The Internet is changing the nature of mediation. It is allowing peer to peer matching of borrowers and savers. It is facilitating new payment protocols and digital currencies. Banks need to evolve and adapt to accommodate these. Most of these questions are empirical in nature. The aim of this paper, however, was to demonstrate that an understanding of the banking model is a prerequisite to understanding how to address these and how to develop hypotheses connected with them.

In conclusion, financial technology is changing the future of banking and the way banks intermediate. It is facilitating digital money and the online transmission of financial assets. It is making banks more customer enteric and more competitive. Scholarly investigation into banking has to adapt. That said, whatever the future, trust will remain at the core of banking. Similarly, deposits and lending will continue to attract regulatory oversight.

Availability of data and materials

Diagrams are my own and the code to reproduce them is available in the supplied Latex files.

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Deep learning in finance and banking: A literature review and classification

• Jian Huang 1 ,
• Junyi Chai   ORCID: orcid.org/0000-0003-1560-845X 2 &
• Stella Cho 2  

Frontiers of Business Research in China volume  14 , Article number:  13 ( 2020 ) Cite this article

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Deep learning has been widely applied in computer vision, natural language processing, and audio-visual recognition. The overwhelming success of deep learning as a data processing technique has sparked the interest of the research community. Given the proliferation of Fintech in recent years, the use of deep learning in finance and banking services has become prevalent. However, a detailed survey of the applications of deep learning in finance and banking is lacking in the existing literature. This study surveys and analyzes the literature on the application of deep learning models in the key finance and banking domains to provide a systematic evaluation of the model preprocessing, input data, and model evaluation. Finally, we discuss three aspects that could affect the outcomes of financial deep learning models. This study provides academics and practitioners with insight and direction on the state-of-the-art of the application of deep learning models in finance and banking.

Introduction

Deep learning (DL) is an advanced technique of machine learning (ML) based on artificial neural network (NN) algorithms. As a promising branch of artificial intelligence, DL has attracted great attention in recent years. Compared with conventional ML techniques such as support vector machine (SVM) and k-nearest neighbors (kNN), DL possesses advantages of the unsupervised feature learning, a strong capability of generalization, and a robust training power for big data. Currently, DL has been applied comprehensively in classification and prediction tasks, computer visions, image processing, and audio-visual recognition (Chai and Li 2019 ). Although DL was developed in the field of computer science, its applications have penetrated diversified fields such as medicine, neuroscience, physics and astronomy, finance and banking (F&B), and operations management (Chai et al. 2013 ; Chai and Ngai 2020 ). The existing literature lacks a good overview of DL applications in F&B fields. This study attempts to bridge this gap.

While DL is the focus of computer vision (e.g., Elad and Aharon 2006 ; Guo et al. 2016 ) and natural language processing (e.g., Collobert et al. 2011 ) in the mainstream, DL applications in F&B are developing rapidly. Shravan and Vadlamani (2016) investigated the tools of text mining for F&B domains. They examined the representative ML algorithms, including SVM, kNN, genetic algorithm (GA), and AdaBoost. Butaru et al. ( 2016 ) compared performances of DL algorithms, including random forests, decision trees, and regularized logistic regression. They found that random forests gained the highest classification accuracy in the delinquency status.

Cavalcante et al. ( 2016 ) summarized the literature published from 2009 to 2015. They analyzed DL models, including multi-layer perceptron (MLP) (a fast library for approximate nearest neighbors), Chebyshev functional link artificial NN, and adaptive weighting NN. Although the study constructed a prediction framework in financial trading, some notable DL techniques such as long short-term memory (LSTM) and reinforcement learning (RL) models are neglect. Thus, the framework cannot ascertain the optimal model in a specific condition.

The reviews of the existing literature are either incomplete or outdated. However, our study provides a comprehensive and state-of-the-art review that could capture the relationships between typical DL models and various F&B domains. We identified critical conditions to limit our collection of articles. We employed academic databases in Science Direct, Springer-Link Journal, IEEE Xplore, Emerald, JSTOR, ProQuest Database, EBSCOhost Research Databases, Academic Search Premier, World Scientific Net, and Google Scholar to search for articles. We used two groups of keywords for our search. One group is related to the DL, including “deep learning,” “neural network,” “convolutional neural networks” (CNN), “recurrent neural network” (RNN), “LSTM,” and “RL.” The other group is related to finance, including “finance,” “market risk,” “stock risk,” “credit risk,” “stock market,” and “banking.” It is important to conduct cross searches between computer-science-related and finance-related literature. Our survey exclusively focuses on the financial application of DL models rather than other DL models like SVM, kNN, or random forest. The time range of our review was set between 2014 and 2018. In this stage, we collected more than 150 articles after cross-searching. We carefully reviewd each article and considered whether it is worthy of entering our pool of articles for review. We removed the articles if they are not from reputable journals or top professional conferences. Moreover, articles were discarded if the details of financial DL models presented were not clarified. Thus, 40 articles were selected for this review eventually.

This study contributes to the literature in the following ways. First, we systematically review the state-of-the-art applications of DL in F&B fields. Second, we summarize multiple DL models regarding specified F&B domains and identify the optimal DL model of various application scenarios. Our analyses rely on the data processing methods of DL models, including preprocessing, input data, and evaluation rules. Third, our review attempts to bridge the technological and application levels of DL and F&B, respectively. We recognize the features of various DL models and highlight their feasibility toward different F&B domains. The penetration of DL into F&B is an emerging trend. Researchers and financial analysts should know the feasibilities of particular DL models toward a specified financial domain. They usually face difficulties due to the lack of connections between core financial domains and numerous DL models. This study will fill this literature gap and guide financial analysts.

The rest of this paper is organized as follows. Section 2 provides a background of DL techniques. Section 3 introduces our research framework and methodology. Section 4 analyzes the established DL models. Section 5 analyzes key methods of data processing, including data preprocessing and data inputs. Section 6 captures appeared criteria for evaluating the performance of DL models. Section 7 provides a general comparison of DL models against identified F&B domains. Section 8 discusses the influencing factors in the performance of financial DL models. Section 9 concludes and outlines the scope for promising future studies.

Background of deep learning

Regarding DL, the term “deep” presents the multiple layers that exist in the network. The history of DL can be traced back to stochastic gradient descent in 1952, which is employed for an optimization problem. The bottleneck of DL at that time was the limit of computer hardware, as it was very time-consuming for computers to process the data. Today, DL is booming with the developments of graphics processing units (GPUs), dataset storage and processing, distributed systems, and software such as Tensor Flow. This section briefly reviews the basic concept of DL, including NN and deep neural network (DNN). All of these models have greatly contributed to the applications in F&B.

The basic structure of NN can be illustrated as Y  =  F ( X T w  +  c ) regarding the independent (input) variables X , the weight terms w , and the constant terms c . Y is the dependent variable and X is formed as an n  ×  m matrix for the number of training sample n and the number of input variables m . To apply this structure in finance, Y can be considered as the price of next term, the credit risk level of clients, or the return rate of a portfolio. F is an activation function that is unique and different from regression models. F is usually formulated as sigmoid functions and tanh functions. Other functions can also be used, including ReLU functions, identity functions, binary step functions, ArcTan functions, ArcSinh functions, ISRU functions, ISRLU functions, and SQNL functions. If we combine several perceptrons in each layer and add a hidden layer from Z 1 to Z 4 in the middle, we term a single layer as a neural network, where the input layers are the X s , and the output layers are the Y s . In finance, Y can be considered as the stock price. Moreover, multiple Y s are also applicable; for instance, fund managers often care about future prices and fluctuations. Figure  1 illustrates the basic structure.

The structure of NN

Based on the basic structure of NN shown in Fig.  1 , traditional networks include DNN, backpropagation (BP), MLP, and feedforward neural network (FNN). Using these models can ignore the order of data and the significance of time. As shown in Fig.  2 , RNN has a new NN structure that can address the issues of long-term dependence and the order between input variables. As financial data in time series are very common, uncovering hidden correlations is critical in the real world. RNN can be better at solving this problem, as compared to other moving average (MA) methods that have been frequently adopted before. A detailed structure of RNN for a sequence over time is shown in Part B of the Appendix (see Fig. 7 in Appendix ).

The abstract structure of RNN

Although RNN can resolve the issue of time-series order, the issue of long-term dependencies remains. It is difficult to find the optimal weight for long-term data. LSTM, as a type of RNN, added a gated cell to overcome long-term dependencies by combining different activation functions (e.g., sigmoid or tanh). Given that LSTM is frequently used for forecasting in the finance literature, we extract LSTM from RNN models and name other structures of standard RNN as RNN(O).

As we focus on the application rather than theoretical DL aspect, this study will not consider other popular DL algorithms, including CNN and RL, as well as Latent variable models such as variational autoencoders and generative adversarial network. Table 6 in Appendix shows a legend note to explain the abbreviations used in this paper. We summarize the relationship between commonly used DL models in Fig.  3 .

Relationships of reviewed DL models for F&B domains

Research framework and methodology

Our research framework is illustrated in Fig.  4 . We combine qualitative and quantitative analyses of the articles in this study. Based on our review, we recognize and identify seven core F&B domains, as shown in Fig.  5 . To connect the DL side and the F&B side, we present our review on the application of the DL model in seven F&B domains in Section 4. It is crucial to analyze the feasibility of a DL model toward particular domains. To do so, we provide summarizations in three key aspects, including data preprocessing, data inputs, and evaluation rules, according to our collection of articles. Finally, we determine optimal DL models regarding the identified domains. We further discuss two common issues in using DL models for F&B: overfitting and sustainability.

The research framework of this study

The identified domains of F&B for DL applications

Figure  5 shows that the application domains can be divided into two major areas: (1) banking and credit risk and (2) financial market investment. The former contains two domains: credit risk prediction and macroeconomic prediction. The latter contains financial prediction, trading, and portfolio management. Prediction tasks are crucial, as emphasized by Cavalcante et al. ( 2016 ). We study this domain from three aspects of prediction, including exchange rate, stock market, and oil price. We illustrate this structure of application domains in F&B.

Figure  6 shows a statistic in the listed F&B domains. We illustrate the domains of financial applications on the X-axis and count the number of articles on the Y-axis. Note that a reviewed article could cover more than one domain in this figure; thus, the sum of the counts (45) is larger than the size of our review pool (40 articles). As shown in Fig.  6 , stock marketing prediction and trading dominate the listed domains, followed by exchange rate prediction. Moreover, we found two articles on banking credit risk and two articles on portfolio management. Price prediction and macroeconomic prediction are two potential topics that deserve more studies.

A count of articles over seven identified F&B domains

Application of DL models in F&B domains

Based on our review, six types of DL models are reported. They are FNN, CNN, RNN, RL, deep belief networks (DBN), and restricted Boltzmann machine (RBM). Regarding FNN, several papers use the alternative terms of backpropagation artificial neural network (ANN), FNN, MLP, and DNN. They have an identical structure. Regarding RNN, one of its well-known models in the time-series analysis is called LSTM. Nearly half of the reviewed articles apply FNN as the primary DL technique. Nine articles apply LSTM, followed by eight articles for RL, and six articles for RNN. Minor ones that are applied in F&B include CNN, DBM, and RBM. We count the number of articles that use various DL models in seven F&B domains, as shown in Table  1 . FNN is the principal model used in exchange rate, price, and macroeconomic predictions, as well as banking default risk and credit. LSTM and FNN are two kinds of popular models for stock market prediction. Differently, RL and FNN are frequently used regarding stock trading. FNN, RL, and simple RNN can be conducted in portfolio management. FNN is the primary model in macroeconomic and banking risk prediction. CNN, LSTM, and RL are emerging research approaches in banking risk prediction. The detailed statistics that contain specific articles can be found in Table 5 in Appendix .

Exchange rate prediction

Shen et al. ( 2015 ) construct an improved DBN model by including RBM and find that their model outperforms the random walk algorithm, auto-regressive-moving-average (ARMA), and FNN with fewer errors. Zheng et al. ( 2017 ) examine the performance of DBN and find that the DBN model estimates the exchange rate better than FNN model does. They find that a small number of layer nodes engender a more significant effect on DBN.

Several scholars believe that a hybrid model should have better performance. Ravi et al. ( 2017 ) contribute a hybrid model by using MLP (FNN), chaos theory, and multi-objective evolutionary algorithms. Their Chaos+MLP + NSGA-II model Footnote 1 has a mean squared error (MSE) with 2.16E-08 that is very low. Several articles point out that only a complicated neural network like CNN can gain higher accuracy. For example, Galeshchuk and Mukherjee ( 2017 ) conduct experiments and claim that a single hidden layer NN or SVM performs worse than a simple model like moving average (MA). However, they find that CNN could achieve higher classification accuracy in predicting the direction of the change of exchange rate because of successive layers of DNN.

Stock market prediction

In stock market prediction, some studies suggest that market news may influence the stock price and DL model, such as using a magic filter to extract useful information for price prediction. Matsubara et al. ( 2018 ) extract information from the news and propose a deep neural generative model to predict the movement of the stock price. This model combines DNN and a generative model. It suggests that this hybrid approach outperforms SVM and MLP.

Minh et al. ( 2017 ) develop a novel framework with two streams combining the gated recurrent unit network and the Stock2vec. It employs a word embedding and sentiment training system on financial news and the Harvard IV-4 dataset. They use the historical price and news-based signals from the model to predict the S&P500 and VN-index price directions. Their model shows that the two-stream gated recurrent unit is better than the gated recurrent unit or the LSTM. Jiang et al. ( 2018 ) establish a recurrent NN that extracts the interaction between the inner-domain and cross-domain of financial information. They prove that their model outperforms the simple RNN and MLP in the currency and stock market. Krausa and Feuerriegel ( 2017 ) propose that they can transform financial disclosure into a decision through the DL model. After training and testing, they point out that LSTM works better than the RNN and conventional ML methods such as ridge regression, Lasso, elastic net, random forest, SVR, AdaBoost, and gradient boosting. They further pre-train words embeddings with transfer learning (Krausa and Feuerriegel 2017 ). They conclude that better performance comes from LSTM with word embeddings. In the sentiment analysis, Sohangir et al. ( 2018 ) compares LSTM, doc2vec, and CNN to evaluate the stock opinions on the StockTwits. They conclude that CNN is the optimal model to predict the sentiment of authors. This result may be further applied to predict the stock market trend.

Data preprocessing is conducted to input data into the NN. Researchers may apply numeric unsupervised methods of feature extraction, including principal component analysis, autoencoder, RBM, and kNN. These methods can reduce the computational complexity and prevent overfitting. After the input of high-frequency transaction data, Chen et al. ( 2018b ) establish a DL model with an autoencoder and an RBM. They compare their model with backpropagation FNN, extreme learning machine, and radial basis FNN. They claim that their model can better predict the Chinese stock market. Chong et al. ( 2017 ) apply the principal component analysis (PCA) and RBM with high-frequency data of the South Korean market. They find that their model can explain the residual of the autoregressive model. The DL model can thus extract additional information and improve prediction performance. More so, Singh and Srivastava ( 2017 ) describe a model involving 2-directional and 2-dimensional (2D 2 ) PCA and DNN. Their model outperforms 2D 2 with radial basis FNN and RNN.

For time-series data, sometimes it is difficult to judge the weight of long-term and short-term data. The LSTM model is just for resolving this problem in financial prediction. The literature has attempted to prove that LSTM models are applicable and outperform conventional FNN models. Yan and Ouyang ( 2017 ) apply LSTM to challenge the MLP, SVM, and kNN in predicting a static and dynamic trend. After a wavelet decomposition and a reconstruction of the financial time series, their model can be used to predict a long-term dynamic trend. Baek and Kim ( 2018 ) apply LSTM not only in predicting the price of S&P500 and KOSPI200 but also in preventing overfitting. Kim and Won ( 2018 ) apply LSTM in the prediction of stock price volatility. They propose a hybrid model that combines LSTM with three generalized autoregressive conditional heteroscedasticity (GARCH)-type models. Hernandez and Abad ( 2018 ) argue that RBM is inappropriate for dynamic data modeling in the time-series analysis because it cannot retain memory. They apply a modified RBM model called p -RBM that can retain the memory of p past states. This model is used in predicting market directions of the NASDAQ-100 index. Compared with vector autoregression (VAR) and LSTM, notwithstanding, they find that LSTM is better because it can uncover the hidden structure within the non-linear data while VAR and p -RBM cannot capture the non-linearity in data.

CNN was established to predict the price with a complicated structure. Making the best use of historical price, Dingli and Fournier ( 2017 ) develop a new CNN model. This model can predict next month’s price. Their results cannot surpass other comparable models, such as logistic regression (LR) and SVM. Tadaaki ( 2018 ) applies the financial ratio and converts them into a “grayscale image” in the CNN model. The results reveal that CNN is more efficient than decision trees (DT), SVM, linear discriminant analysis, MLP, and AdaBoost. To predict the stock direction, Gunduz et al. ( 2017 ) establish a CNN model with a so-called specially ordered feature set whose classifier outperforms either CNN or LR.

Stock trading

Many studies adopt the conventional FNN model and try to set up a profitable trading system. Sezer et al. ( 2017 ) combine GA with MLP. Chen et al. ( 2017 ) adopt a double-layer NN and discover that its accuracy is better than ARMA-GARCH and single-layer NN. Hsu et al. ( 2018 ) equip the Black-Scholes model and a three-layer fully-connected feedforward network to estimate the bid-ask spread of option price. They argue that this novel model is better than the conventional Black-Scholes model with lower RMSE. Krauss et al. ( 2017 ) apply DNN, gradient-boosted-trees, and random forests in statistical arbitrage. They argue that their returns outperform the market index S&P500.

Several studies report that RNN and its derivate models are potential. Deng et al. ( 2017 ) extend the fuzzy learning into the RNN model. After comparing their model to different DL models like CNN, RNN, and LSTM, they claim that their model is the optimal one. Fischer and Krauss ( 2017 ) and Bao et al. ( 2017 ) argue that LSTM can create an optimal trading system. Fischer and Krauss ( 2017 ) claim that their model has a daily return of 0.46 and a sharp ratio of 5.8 prior to the transaction cost. Given the transaction cost, however, LSTM’s profitability fluctuated around zero after 2010. Bao et al. ( 2017 ) advance Fischer and Krauss’s ( 2017 ) work and propose a novel DL model (i.e., WSAEs-LSTM model). It uses wavelet transforms to eliminate noise, stacked autoencoders (SAEs) to predict stock price, and LSTM to predict the close price. The result shows that their model outperforms other models such as WLSTM, Footnote 2 LSTM, and RNN in predictive accuracy and profitability.

RL is popular recently despite its complexity. We find that five studies apply this model. Chen et al. ( 2018a ) propose an agent-based RL system to mimic 80% professional trading strategies. Feuerriegel and Prendinger ( 2016 ) convert the news sentiment into the signal in the trading system, although their daily returns and abnormal returns are nearly zero. Chakraborty ( 2019 ) cast the general financial market fluctuation into a stochastic control problem and explore the power of two RL models, including Q-learning Footnote 3 and state-action-reward-state-action (SARSA) algorithm. Both models can enhance profitability (e.g., 9.76% for Q-learning and 8.52% for SARSA). They outperform the buy-and-hold strategy. Footnote 4 Zhang and Maringer ( 2015 ) conduct a hybrid model called GA, with recurrent RL. GA is used to select an optimal combination of technical indicators, fundamental indicators, and volatility indicators. The out-of-sample trading performance is improved due to a significantly positive Sharpe ratio. Martinez-Miranda et al. ( 2016 ) create a new topic of trading. It uses a market manipulation scanner model rather than a trading system. They use RL to model spoofing-and-pinging trading. This study reveals that their model just works on the bull market. Jeong and Kim ( 2018 ) propose a model called deep Q-network that is constructed by RL, DNN, and transfer learning. They use transfer learning to solve the overfitting issue incurred as a result of insufficient data. They argue that the profit yields in this system increase by four times the amount in S&P500, five times in KOSPI, six times in EuroStoxx50, and 12 times in HIS.

Banking default risk and credit

Most articles in this domain focus on FNN applications. Rönnqvist and Sarlin ( 2017 ) propose a model for detecting relevant discussions in texting and extracting natural language descriptions of events. They convert the news into a signal of the bank-distress report. In their back-test, their model reflects the distressing financial event of the 2007–2008 period.

Zhu et al. ( 2018 ) propose a hybrid CNN model with a feature selection algorithm. Their model outperforms LR and random forest in consumer credit scoring. Wang et al. ( 2019 ) consider that online operation data can be used to predict consumer credit scores. They thus convert each kind of event into a word and apply the Event2vec model to transform the word into a vector in the LSTM network. The probability of default yields higher accuracy than other models. Jurgovsky et al. ( 2018 ) employs the LSTM to detect credit card fraud and find that LSTM can enhance detection accuracy.

Han et al. ( 2018 ) report a method that adopts RL to assess the credit risk. They claim that high-dimensional partial differential equations (PDEs) can be reformulated by using backward stochastic differential equations. NN approximates the gradient of the unknown solution. This model can be applied to F&B risk evaluation after considering all elements such as participating agents, assets, and resources, simultaneously.

Portfolio management

Song et al. ( 2017 ) establish a model after combining ListNet and RankNet to make a portfolio. They take a long position for the top 25% stocks and hold the short position for the bottom 25% stocks weekly. The ListNetlong-short model is the optimal one, which can achieve a return of 9.56%. Almahdi and Yang ( 2017 ) establish a better portfolio with a combination of RNN and RL. The result shows that the proposed trading system respond to transaction cost effects efficiently and outperform hedge fund benchmarks consistently.

Macroeconomic prediction

Sevim et al. ( 2014 ) develops a model with a back-propagation learning algorithm to predict the financial crises up to a year before it happened. This model contains three-layer perceptrons (i.e., MLP) and can achieve an accuracy rate of approximately 95%, which is superior to DT and LR. Chatzis et al. ( 2018 ) examine multiple models such as classification tree, SVM, random forests, DNN, and extreme gradient boosting to predict the market crisis. The results show that crises encourage persistence. Furthermore, using DNN increases the classification accuracy that makes global warning systems more efficient.

Price prediction

For price prediction, Sehgal and Pandey ( 2015 ) review ANN, SVM, wavelet, GA, and hybrid systems. They separate the time-series models into stochastic models, AI-based models, and regression models to predict oil prices. They reveal that researchers prevalently use MLP for price prediction.

Data preprocessing and data input

Data preprocessing.

Data preprocessing is conducted to denoise before data training of DL. This section summarizes the methods of data preprocessing. Multiple preprocessing techniques discussed in Part 4 include the principal component analysis (Chong et al. 2017 ), SVM (Gunduz et al. 2017 ), autoencoder, and RBM (Chen et al. 2018b ). There are several additional techniques of feature selection as follows.

Relief: The relief algorithm (Zhu et al. 2018 ) is a simple approach to weigh the importance of the feature. Based on NN algorithms, relief repeats the process for n times and divides each final weight vector by n . Thus, the weight vectors are the relevance vectors, and features are selected if their relevance is larger than the threshold τ .

Wavelet transforms: Wavelet transforms are used to fix the noise feature of the financial time series before feeding into a DL network. It is a widely used technique for filtering and mining single-dimensional signals (Bao et al. 2017 ).

Chi-square: Chi-square selection is commonly used in ML to measure the dependence between a feature and a class label. The representative usage is by Gunduz et al. ( 2017 ).

Random forest: Random forest algorithm is a two-stage process that contains random feature selection and bagging. The representative usage is by Fischer and Krauss ( 2017 ).

Data inputs

Data inputs are an important criterion for judging whether a DL model is feasible for particular F&B domains. This section summarizes the method of data inputs that have been adopted in the literature. Based on our review, five types of input data in the F&B domain can be presented. Table  2 provides a detailed summary of the input variable in F&B domains.

History price: The daily exchange rate can be considered as history price. The price can be the high, low, open, and close price of the stock. Related articles include Bao et al. ( 2017 ), Chen et al. ( 2017 ), Singh and Srivastava ( 2017 ), and Yan and Ouyang ( 2017 ).

Technical index: Technical indexes include MA, exponential MA, MA convergence divergence, and relative strength index. Related articles include Bao et al. ( 2017 ), Chen et al. ( 2017 ), Gunduz et al. ( 2017 ), Sezer et al. ( 2017 ), Singh and Srivastava ( 2017 ), and Yan and Ouyang ( 2017 ).

Financial news: Financial news covers financial message, sentiment shock score, and sentiment trend score. Related articles include Feuerriegel and Prendinger ( 2016 ), Krausa and Feuerriegel ( 2017 ), Minh et al. ( 2017 ), and Song et al. ( 2017 ).

Financial report data: Financial report data can account for items in the financial balance sheet or the financial report data (e.g., return on equity, return on assets, price to earnings ratio, and debt to equity ratio). Zhang and Maringer ( 2015 ) is a representative study on the subject.

Macroeconomic data: This kind of data includes macroeconomic variables. It may affect elements of the financial market, such as exchange rate, interest rate, overnight interest rate, and gross foreign exchange reserves of the central bank. Representative articles include Bao et al. ( 2017 ), Kim and Won ( 2018 ), and Sevim et al. ( 2014 ).

Stochastic data: Chakraborty ( 2019 ) provides a representative implementation.

Evaluation rules

It is critical to judge whether an adopted DL model works well in a particular financial domain. We, thus, need to consider evaluation systems of criteria for gauging the performance of a DL model. This section summarizes the evaluation rules of F&B-oriented DL models. Based on our review, three evaluation rules dominate: the error term, the accuracy index, and the financial index. Table  3 provides a detailed summary. The evaluation rules can be boiled down to the following categories.

Error term: Suppose Y t  +  i and F t  +  i are the real data and the prediction data, respectively, where m is the total number. The following is a summary of the functional formula commonly employed for evaluating DL models.

Mean Absolute Error (MAE): \( {\sum}_{i=1}^m\frac{\left|{Y}_{t+i}-{F}_{t+i}\right|}{m} \) ;

Mean Absolute Percent Error (MAPE): \( \frac{100}{m}{\sum}_{i=1}^m\frac{\left|{Y}_{t+i}-{F}_{t+i}\right|}{Y_{t+i}} \) ;

Mean Squared Error (MSE): \( {\sum}_{i=1}^m\frac{{\left({Y}_{t+i}-{F}_{t+i}\right)}^2}{m} \) ;

Root Mean Squared Error (RMSE): \( \sqrt{\sum_{i=1}^m\frac{{\left({Y}_{t+i}-{F}_{t+i}\right)}^2}{m}} \) ;

Normalized Mean Square Error (NMSE): \( \frac{1}{m}\frac{\sum {\left({Y}_{t+i}-{F}_{t+i}\right)}^2}{\mathit{\operatorname{var}}\left({Y}_{t+i}\right)} \) .

Accuracy index: According to Matsubara et al. ( 2018 ), we use TP, TN, FP, and FN to represent the number of true positives, true negatives, false positives, and false negatives, respectively, in a confusion matrix for classification evaluation. Based on our review, we summarize the accuracy indexes as follows.

Directional Predictive Accuracy (DPA): \( \frac{1}{N}{\sum}_{t=1}^N{D}_t \) , if ( Y t  + 1  −  Y t ) × ( F t  + 1  −  Y t ) ≥ 0, D t  = 1, otherwise, D t  = 0;

Actual Correlation Coefficient (ACC): \( \frac{TP+ TN}{TP+ FP+ FN+ TN} \) ;

Matthews Correlation Coefficient (MCC): \( \frac{TP\times TN- FP\times FN}{\sqrt{\left( TP+ FP\right)\left( TP+ FN\right)\left( TN+ FP\right)\left( TN+ FN\right)}} \) .

Financial index: Financial indexes involve total return, Sharp ratio, abnormal return, annualized return, annualized number of transaction, percentage of success, average profit percent per transaction, average transaction length, maximum profit percentage in the transaction, maximum loss percentage in the transaction, maximum capital, and minimum capital.

For the prediction by regressing the numeric dependent variables (e.g., exchange rate prediction or stock market prediction), evaluation rules are mostly error terms. For the prediction by classification in the category data (e.g., direction prediction on oil price), the accuracy indexes are widely conducted. For stock trading and portfolio management, financial indexes are the final evaluation rules.

General comparisons of DL models

This study identifies the most efficient DL model in each identified F&B domain. Table  4 illustrates our comparisons of the error terms in the pool of reviewed articles. Note that “A > B” means that the performance of model A is better than that of model B. “A + B” indicates the hybridization of multiple DL models.

At this point, we have summarized three methods of data processing in DL models against seven specified F&B domains, including data preprocessing, data inputs, and evaluation rules. Apart from the technical level of DL, we find the following:

NN has advantages in handling cross-sectional data;

RNN and LSTM are more feasible in handling time series data;

CNN has advantages in handling the data with multicollinearity.

Apart from application domains, we can induce the following viewpoints. Cross-sectional data usually appear in exchange rate prediction, price prediction, and macroeconomic prediction, for which NN could be the most feasible model. Time series data usually appear in stock market prediction, for which LSTM and RNN are the best options. Regarding stock trading, a feasible DL model requires the capabilities of decision and self-learning, for which RL can be the best. Moreover, CNN is more suitable for the multivariable environment of any F&B domains. As shown in the statistics of the Appendix , the frequency of using corresponding DL models corresponds to our analysis above. Selecting proper DL models according to the particular needs of financial analysis is usually challenging and crucial. This study provides several recommendations.

We summarize emerging DL models in F&B domains. Nevertheless, can these models refuse the efficient market hypothesis (EMH)? Footnote 5 According to the EMH, the financial market has its own discipline. There is no long-term technical tool that could outperform an efficient market. If so, using DL models may not be practical in long-term trading as it requires further experimental tests. However, why do most of the reviewed articles argue that their DL models of trading outperform the market returns? This argument has challenged the EMH. A possible explanation is that many DL algorithms are still challenging to apply in the real-world market. The DL models may raise trading opportunities to gain abnormal returns in the short-term. In the long run, however, many algorithms may lose their superiority, whereas EMH still works as more traders recognize the arbitrage gap offered by these DL models.

This section discusses three aspects that could affect the outcomes of DL models in finance.

Training and validation of data processing

The size of the training set.

The optimal way to improve the performance of models is by enhancing the size of the training data. Bootstrap can be used for data resampling, and generative adversarial network (GAN) can extend the data features. However, both can recognize numerical parts of features. Sometimes, the sample set is not diverse enough; thus, it loses its representativeness. Expanding the data size could make the model more unstable. The current literature reported diversified sizes of training sets. The requirements of data size in the training stage could vary by different F&B tasks.

The number of input factors

Input variables are independent variables. Based on our review, multi-factor models normally perform better than single-factor models in the case that the additional input factors are effective. In the time-series data model, long-term data have less prediction errors than that for a short period. The number of input factors depends on the employment of the DL structure and the specific environment of F&B tasks.

The quality of data

Several methods can be used to improve the data quality, including data cleaning (e.g., dealing with missing data), data normalization (e.g., taking the logarithm, calculating the changes of variables, and calculating the t -value of variables), feature selection (e.g., Chi-square test), and dimensionality reduction (e.g., PCA). Financial DL models require that the input variables should be interpretable in economics. When inputting the data, researchers should clarify the effective variables and noise. Several financial features, such as technical indexes, are likely to be created and added into the model.

Selection on structures of DL models

DL model selection should depend on problem domains and cases in finance. NN is suitable for processing cross-sectional data. LSTM and other RNNs are optimal choices for time-series data in prediction tasks. CNN can settle the multicollinearity issue through data compression. Latent variable models like GAN can be better for dimension reduction and clustering. RL is applicable in the cases with judgments like portfolio management and trading. The return levels and outcomes on RL can be affected significantly by environment (observation) definitions, situation probability transfer matrix, and actions.

The setting of objective functions and the convexity of evaluation rules

Objective function selection affects training processes and expected outcomes. For predictions on stock price, low MAE merely reflects the effectiveness of applied models in training; however, it may fail in predicting future directions. Therefore, it is vital for additional evaluation rules for F&B. Moreover, it can be more convenient to resolve the objective functions if they are convex.

The influence of overfitting (underfitting)

Overfitting (underfitting) commonly happens in using DL models, which is clearly unfavorable. A generated model performs perfectly in one case but usually cannot replicate good performance with the same model and identical coefficients. To solve this problem, we have to trade off the bias against variances. Bias posits that researchers prefer to keep it small to illustrate the superiority of their models. Generally, a deeper (i.e., more layered) NN model or neurons can reduce errors. However, it is more time-consuming and could reduce the feasibility of applied DL models.

One solution is to establish validation sets and testing sets for deciding the numbers of layers and neurons. After setting optimal coefficients in the validation set (Chong et al. 2017 ; Sevim et al. 2014 ), the result in the testing sets reveals the level of errors that could mitigate the effect of overfitting. One can input more samples of financial data to check the stability of the model’s performance. This method is known as the early stopping. It stops training more layers in the network once the testing result has achieved an optimal level.

Moreover, regularization is another approach to conquer the overfitting. Chong et al. ( 2017 ) introduces a constant term for the objective function and eventually reduces the variates of the result. Dropout is also a simple method to address overfitting. It reduces the dimensions and layers of the network (Minh et al. 2017 ; Wang et al. 2019 ). Finally, the data cleaning process (Baek and Kim 2018 ; Bao et al. 2017 ), to an extent, could mitigate the impact of overfitting.

Financial models

The sustainability of the model.

According to our reviews, the literature focus on evaluating the performance of historical data. However, crucial problems remain. Given that prediction is always complicated, the problem of how to justify the robustness of the used DL models in the future remains. More so, whether a DL model could survive in dynamic environments must be considered.

The following solutions could be considered. First, one can divide the data into two groups according to the time range; performance can subsequently be checked (e.g., using the data for the first 3 years to predict the performance of the fourth year). Second, the feature selection can be used in the data preprocessing, which could improve the sustainability of models in the long run. Third, stochastic data can be generated for each input variable by fixing them with a confidence interval, after which a simulation to examine the robustness of all possible future situations is conducted.

The popularity of the model

Whether a DL model is effective for trading is subject to the popularity of the model in the financial market. If traders in the same market conduct an identical model with limited information, they may run identical results and adopt the same trading strategy accordingly. Thus, they may lose money because their strategy could sell at a lower price after buying at a higher.

Conclusion and future works

Concluding remarks.

This paper provides a comprehensive survey of the literature on the application of DL in F&B. We carefully review 40 articles refined from a collection of 150 articles published between 2014 and 2018. The review and refinement are based on a scientific selection of academic databases. This paper first recognizes seven core F&B domains and establish the relationships between the domains and their frequently-used DL models. We review the details of each article under our framework. Importantly, we analyze the optimal models toward particular domains and make recommendations according to the feasibility of various DL models. Thus, we summarize three important aspects, including data preprocessing, data inputs, and evaluation rules. We further analyze the unfavorable impacts of overfitting and sustainability when applying DL models and provide several possible solutions. This study contributes to the literature by presenting a valuable accumulation of knowledge on related studies and providing useful recommendations for financial analysts and researchers.

Future works

Future studies can be conducted from the DL technical and F&B application perspectives. Regarding the perspective of DL techniques, training DL model for F&B is usually time-consuming. However, effective training could greatly enhance accuracy by reducing errors. Most of the functions can be simulated with considerable weights in complicated networks. First, one of the future works should focus on data preprocessing, such as data cleaning, to reduce the negative effect of data noise in the subsequent stage of data training. Second, further studies on how to construct layers of networks in the DL model are required, particularly when considering a reduction of the unfavorable effects of overfitting and underfitting. According to our review, the comparisons between the discussed DL models do not hinge on an identical source of input data, which renders these comparisons useless. Third, more testing regarding F&B-oriented DL models would be beneficial.

In addition to the penetration of DL techniques in F&B fields, more structures of DL models should be explored. From the perspective of F&B applications, the following problems need further research to investigate desirable solutions. In the case of financial planning, can a DL algorithm transfer asset recommendations to clients according to risk preferences? In the case of corporate finance, how can a DL algorithm benefit capital structure management and, thus, maximize the values of corporations? How can managers utilize DL technical tools to gauge the investment environment and financial data? How can they use such tools to optimize cash balances and cash inflow and outflow? Until recently, DL models like RL and generative adversarial networks are rarely used. More investigations on constructing DL structures for F&B regarding preferences would be beneficial. Finally, the developments of professional F&B software and system platforms that implement DL techniques are highly desirable.

Availability of data and materials

Not applicable.

In the model, NSGA stands for non-dominated sorting genetic algorithm.

A combination of Wavelet transforms (WT) and long-short term memory (LSTM) is called WLSTM in Bao et al. ( 2017 ).

Q-learning is a model-free reinforcement learning algorithm.

Buy-and-hold is a passive investment strategy in which an investor buys stocks (or ETFs) and holds them for a long period regardless of fluctuations in the market.

EMH was developed from a Ph.D. dissertation by economist Eugene Fama in the 1960s. It says that at any given time, stock prices reflect all available information and trade at exactly their fair value at all times. It is impossible to consistently choose stocks that will beat the returns of the overall stock market. Therefore, this hypothesis implies that the pursuit of market-beating performance is more about chance than it is about researching and selecting the right stocks.

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Acknowledgments

The constructive comments of the editor and three anonymous reviewers on an earlier version of this paper are greatly appreciated. The authors are indebted to seminar participants at 2019 China Accounting and Financial Innovation Form at Zhuhai for insightful discussions. The corresponding author thanks the financial supports from BNU-HKBU United International College Research Grant under Grant R202026.

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Part A. Summary of publications in DL and F&B domains

Part b. detailed structure of standard rnn.

The abstract structure of RNN for a sequence cross over time can be extended, as shown in Fig. 7 in Appendix , which presents the inputs as X , the outputs as Y , the weights as w , and the Tanh functions.

The detailed structure of RNN

Part C. List of abbreviations

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Huang, J., Chai, J. & Cho, S. Deep learning in finance and banking: A literature review and classification. Front. Bus. Res. China 14 , 13 (2020). https://doi.org/10.1186/s11782-020-00082-6

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Article Contents

1. introduction, 2. developments in global banking, pre- and post-gfc, 3. global banking: benefits and risks, 4. global banking: regulation and policies, 5. conclusions and areas for possible research.

• < Previous

Global Banking: Recent Developments and Insights from Research *

This paper is in part based on a presentation at the TCH Symposium, Imperial College, London, April 20–21, 2015. I would like to thank the attendees for their feedback; Franklin Allen for suggesting that I write the paper; and Franklin Allen, Ricardo Correa, Neeltje van Horen, Friederike Niepmann, and especially the two referees for their very useful comments. The opinions expressed are my own and do not necessarily reflect the views of the Board of Governors of the Federal Reserve System.

• Article contents
• Figures & tables
• Supplementary Data

Stijn Claessens, Global Banking: Recent Developments and Insights from Research, Review of Finance , Volume 21, Issue 4, July 2017, Pages 1513–1555, https://doi.org/10.1093/rof/rfw045

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Following recent crises, cross-border capital flows have declined considerably, and many advanced countries’ banks are retrenching. At the same time, banks from emerging and developing countries continue to expand abroad, and banking has become more regional. Research highlights that long-term debt flows are less volatile and that foreign banks with larger presence, more domestic funding, and closer relationships provide more finance and share risks better. While ongoing changes in global banking influence its overall benefits, the crises also revealed the need for a consistent framework for supervising and resolving globally active banks, with the European Banking Union an important model.

Global banking is going through some profound changes following the global financial crisis (GFC) and the subsequent euro-area crisis. The crises have led to large balance sheet impairments, notably for many banks in advanced countries. They have also led to a barrage of new regulations, tighter supervision and oversight, and some banks having to pay large penalties for past wrongdoings. And, to some degree, the crises have sharpened market discipline and have made investors and creditors more wary of banks’ activities, including their international operations. Together, these developments have forced banks to raise new capital; deleverage their balance sheets, including international; and pare back cost structures by shedding activities and personnel and adjusting compensation. Other, more secular developments include new entries in financial services provision spurred by advances in delivering financial services using digital means, which are putting additional pressures on existing financial institutions. In addition, there has been a trend increase in the importance of emerging markets and developing countries in the world economy in general and in finance specifically, including through greater cross-border bank flows and direct foreign bank presence.

As these changes continue to unfold and institutions adjust, they are affecting the structure and industrial organization of global banking. In turn, they have consequences for the benefits and risks that global banking brings to financial systems and economies. As such, it is a useful time to take stock of what has changed in global banking since the GFC, what the (recent) literature has found regarding the benefits and risks of global banking, and what developments underway may mean for (possible changes in) regulation, supervision, and other policies so as to ensure that the best balance is struck between benefits and risks, considering also countries’ characteristics and circumstances. This is what this paper sets out to do.

The paper starts by reviewing the forms in which trade in banking services can occur, narrowing the focus to two forms: direct cross-border bank lending and foreign bank presence (bricks and mortar). It then reviews the state of affairs in global banking in both forms, and the interactions between the two, before and since the GFC. Before the GFC, there was a sharp increase in global banking, in both capital flows and foreign bank presence, with many countries becoming financially more integrated. The clearest effect of the GFC has been on cross-border bank claims, with the overall stock some 20% lower, with this reduction the greatest in the euro zone. The trend in foreign bank presence differs. While many banks from advanced countries have closed or sold off their (far-flung) foreign subsidiaries, banks from emerging markets and developing countries often continue to invest abroad. Overall, foreign bank presence is not much less than before the GFC, and the effects of crises and underlying trends, although accelerated, have resulted in a more regionalized but not more fragmented system.

The paper then reviews the literature on the benefits and risks of global banking. Research has found that the benefits and risks of banking flows can vary by source, destination, and type of flow and that the benefits and risks of foreign banks can vary by bank, home, host, and bilateral characteristics. Similar to capital flows in general, if countries meet certain thresholds—macroeconomic, economic, and financial—banking flows are likely both greater and more beneficial. In terms of presence, foreign banks more likely have positive effects on financial systems and economies if they are more developed, with less barriers to capital flows and entry. Other thresholds exist, as when greater presence and foreign banks with larger market share mean greater beneficial effects, including on small- and medium-sized enterprises’ (SMEs) access to finance. Also, healthier parent banks mean more and better local credit. And although foreign banks can “cherry pick” borrowers, as has been found for low-income countries, this is not the case when the foreign banks are from home countries close by and have a large presence locally.

In terms of possible adverse effects on overall financial and economic stability, the literature has highlighted that risk-sharing through global banking has two sides. On the one hand, local risks are more diversified internationally and global banks can support their foreign affiliates during periods of stress in the host market. But, on the other hand, (funding) shocks to parent banks and home countries can be transmitted, including to foreign affiliates, and negatively affect local lending and economic activity. Research shows that the degree of shock transmission varies importantly by host and home country circumstances and bank characteristics. When close by, for example, foreign banks tend to support local operations more when local shocks occur. And while foreign banks tend to cut back local lending more than domestic banks do during periods of global stress, they do not do so compared with internationally funded domestic banks, and lending is more stable when foreign banks are large locally and rely more on local deposit-taking.

The paper lastly reviews regulatory challenges regarding global banks highlighted by the GFC and the implications of the increased role of emerging markets’ and developing countries’ banks and the greater regionalization in banking. Although the trend remains for formally open markets in financial services, some countries have put in place regulatory limits on the movement of capital and liquidity within banking groups. Many new rules aimed at global systemically important banks (G-SIBs) have been enacted, international coordination in supervising G-SIBs has been enhanced, and, importantly, dealing with their potential failures is now recognized as crucial. With global banking becoming more regional, coordination in all of these dimensions—regulation, supervision, and resolution—could be easier, and the European Banking Union (BU) is a good example of progress. But much remains to be done, notably on resolution of internationally active banks, including on modalities for liquidity support and burden sharing.

The outline of the paper is as follows. Section 2 describes developments in global banking, covering both cross-border capital flows and foreign bank presence, as well as their interactions, before and after the GFC. It also reviews the literature on what has driven changes since the GFC, distinguishing supply, demand, and regulatory factors. Section 3 reviews the benefits and risks of global banking, analyzing, among other factors, effects on domestic financial development, access to finance, and relationships between local lending and cross-border banking flows. In terms of financial stability, it reviews evidence on risk-sharing covering both the exporting and importing of financial shocks. Section 4 reviews the policy agenda on why global banks can give rise to systemic risks, both national and cross-border, and what those risks imply for reforms. It reviews measures taken to reduce the systemic risks related to global banks, focusing on progress in resolving G-SIBs in distress and emphasizing the so-called financial trilemma—that is, the incompatibility between unrestricted cross-border banking, national financial, and regulatory independence, and overall financial stability and the choices that consequently need to be made. Section 5 concludes and lists some outstanding research questions.

This section first reviews the state of global banking before the GFC, focusing on the two most important forms of trade in banking services, cross-border bank flows, and foreign bank presence. It then describes how global banking has changed since the GFC, highlighting the large effects of balance sheet impairments and regulatory changes for banks from advanced countries and the more secular increase in the role of banks from emerging markets and developing countries.

When discussing global banking, it is useful to start with reviewing the forms in which trade in financial services can occur. As commonly used by the World Trade Organization, trade in (financial) services can encompass one of four forms: (1) cross-border claims/flows, e.g., lending and deposit-taking, but also (re-)insurance; (2) consumption abroad, including through the movement of consumers to the territory of suppliers, e.g., the purchase of financial services by consumers while traveling abroad; (3) financial foreign direct investment (FDI), in the form of a foreign bank, insurance, etc., which can be in the form of a subsidiary or a branch and materialize through mergers and acquisitions of existing banks or new investments; and (4) supply of services through the physical presence of persons, such as independent financial consultants or bank managers, of one country in another. Of these four modes, the second, consumption abroad, and the fourth, trade in person, are quantitatively of little importance for banking and also raise few policy issues. As such, research has focused on cross-border bank flows (mode 1) and foreign bank presence (mode 3), which is also what I do.

2.1 Developments Pre-GFC

2.1.a. cross-border banking flows.

The pre-crisis period saw a large increase in financial globalization, including through cross-border banking and foreign bank presence, which is well documented, notably by Lane and Milesi-Ferretti (2001 , 2007 ) and subsequent updates to their database. Increases coincided with the general globalization over this period, including in trade and FDI in goods and services. Financial globalization reflected not only market forces, but also deregulation as countries, both emerging and developing, (further) opened their capital account (see Fernández et al. [2015] for data on the evolution of countries’ de jure capital account openness). Indeed, while countries that liberalized saw greater increases in cross-border assets and liabilities, those others that did not liberalize saw large increases as well. All in all, the stock of cross-border bank claims (in real 2007 dollars) increased two-fold over the period 1995–2007. And, as Figure 1 using Bank for International Settlements (BIS) International Banking Statistics (IBS) data shows, international banking claims, including both direct cross-border lending and local lending by foreign banks’ subsidiaries abroad, were on a sharply increasing trend just before the GFC.

Cross-border and local claims (Trillions USD).

Notes : Local claims are claims of foreign banks’ affiliates, including subsidiaries and branches. Claims can be in foreign and local currency.

Source : BIS International Banking Statistics, consolidated data on an ultimate risk basis.

The trend of increased financial globalization was not uniform, however, and there were distinct patterns in terms of lending and borrowing countries and bilateral and regional patterns. Clearly, banks in advanced countries were at the forefront of cross-border lending. Among these, U.S. banks were less aggressive over this period than in earlier periods, while many (smaller) European countries became large lenders. In terms of recipients, advanced countries were again the most important, and assets and liabilities among advanced countries often offset each other, i.e., while gross flows were large, net flows were much smaller. For emerging markets, offsets were much less, and many were net recipients of large capital flows, at least until the GFC.

In terms of bilateral patterns, distance—geographical, institutional, and cultural—has, similar to banks domestically, been found to be an important variable in explaining the pattern of bilateral bank flows ( Portes, Rey, and Oh, 2001 ) and other capital flows ( Portes and Rey, 2005 ). 1 Physical distance explains in part the regional concentration in bank flows, but it can be a poor proxy for informational and other (financial) frictions, as the large flows among some advanced countries that are not physically—but, instead, institutionally—close show. And, regardless, “distance” is clearly not the only factor. The very large intra-euro bank flows, for example, reflect not just close distances, but also close economic, monetary, and political integration.

2.1.b. Foreign bank presence

Over this period, financial globalization increasingly happened through foreign bank presence ( Figure 2 ), with increases in market share especially high in emerging markets and developing countries ( Figure 3 ). Similar to the developments in bank flows, this trend was triggered by multiple factors ( Claessens and van Horen, 2014b ). One specific factor was the banking system privatization in many regions and related sale of banks to foreigners. In East Asia and Latin America, this privatization happened following their crises in the late 1990s, whereas in Central and Eastern Europe, it followed their transition to market-based economies, as well as some crises, in the early 1990s.

Numbers, and number and asset shares of foreign banks.

Source: Claessens and van Horen (2015) .

Foreign bank presence, overall and by income groups (number shares).

Notes : “OECD” includes all core OECD countries. “Emerging markets” consists of all countries that are included in the Standard & Poor’s Emerging Market and Frontier Markets indexes and that were not high-income countries in 2000. “Developing countries” includes all other countries.

Source : Claessens and van Horen (2015) .

As a consequence, foreign bank presence became very large in some emerging markets, with market shares (in terms of the number of banks) in 2007 exceeding 80% in 14 countries and more than 50% in 63 out of 118 countries. There was a large variation, though, as foreign market shares in some emerging markets were less than 10% (besides Cuba and Ethiopia, e.g., Saudi Arabia and Haiti). Also, foreign bank presence remained low in many advanced countries—e.g., in half, it was less than 25%. In fact, host country’s gross domestic product (GDP) per capita and foreign bank presence are negatively correlated (−0.45). Moreover, when small in terms of number shares, foreign banks are more likely niche players, capturing an even smaller share of assets, whereas when large in terms of number shares, they are likely even more important with respect to financial intermediation, capturing a larger share of assets. This pattern reinforces foreign banks’ greater role in emerging markets compared with in advanced countries.

In terms of home countries, there has been a very high concentration, at least until the mid-2000s, as a few countries have large foreign bank “exports.” Notably, the majority (66%) of foreign banks as of 2007 were owned by banks from North America, mostly the USA, and Western Europe, mostly the UK. Nevertheless, already in 2007, parent banks from emerging markets and developing countries started to invest abroad, and their shares in numbers (19% and 7%, respectively) were non-negligible.

Research has identified several factors, besides differences in the removal of entry restrictions and privatizations, that help explain the degree of foreign bank penetration ( Claessens and van Horen, 2014a , review). Earlier studies found that investment tends to correlate with trade and general FDI flows, which can be multinational companies or other forms of FDI, indicating that foreign banks tend to follow as well as lead their customers (for the latter relationship, see Poelhekke, 2015 ). Host country expected economic growth and local bank inefficiencies, as well as low costs and efficient regulations, are also important drivers. Also, as Niepmann (2015) models, banks might engage in cross-border banking versus foreign bank presence given differences in relative factor endowments and the efficiency of banking sectors. Besides having access to clients with sufficient growth potential and an institutional environment where claims can be legally enforced, being able to acquire and use information efficiently has been found to be important. And a number of studies show foreign investment to be greater when countries are “closer”—either geographically, culturally, or institutionally—likely as it eases the ability to manage from afar and transfer soft information within the banking group. 2

As with other capital flows, there are strong bilateral patterns in foreign investments. Table I shows that as of 2009, banks from Organisation for Economic Co-operation and Development (OECD) countries (the biggest investors) tend to invest mostly in other OECD countries or emerging markets. And banks from emerging markets tend to invest in other emerging markets or developing countries, while banks from developing countries tend to invest in other developing countries or emerging markets. So banks seem to seek out host countries that are relatively similar to or lower than, in terms of income levels and institutional development, their home market. A related finding highlights the strong regional patterns in foreign bank presence. Splitting countries into four broad geographical regions that cut across income groups (America, Asia, Europe, and the Middle East and Africa) shows that the majority of foreign banks come from within each region ( Figure 4 ), with the highest intraregional share for the Middle East and Africa, more than 70%. Importantly, this pattern has become stronger over time.

Regional asset shares, 2007 and 2013.

Notes : Countries are grouped in four geographical regions irrespective of the income level of the countries. “America” includes Canada, the USA, and all countries in Latin America and the Caribbean; “Europe” includes all Western and Eastern European countries; “Asia” includes all countries in Central, East, and South Asia and the Pacific countries including Japan, Australia, and New Zealand; “Middle East and Africa” includes all countries in the Middle East and North and Sub-Saharan Africa.

Number and share of foreign banks from the home country group present in the host country group

Notes : “OECD” includes all core OECD countries. “Other high-income countries” include all countries classified as high income by the World Bank in 2000 but not belonging to the OECD. “Emerging markets” include all countries that are included in the Standard and Poor’s Emerging Market and Frontier Markets indexes and that were not high-income countries in 2000. “Developing countries” include all other countries.

Source : Claessens and van Horen (2014b) .

2.2 Developments of Post-GFC

Post-GFC, a general view is that global banking has become more fragmented. This view is captured in headlines such as those of the Economist : “Since 2008 global financial integration has gone into reverse” (October 2013, special report on the World Economy). It is also evident in more in-depth analysis, such as that of the European Central Bank (ECB): “Some banks have resumed their cross-border activities, but the level of integration in the banking markets remains lower than before the financial crisis” (April 2014 , page 28). Analyses, however, show that while increased fragmentation applies somewhat to cross-border claims, it is less relevant to foreign bank presence, where the GFC has rather accelerated a more secular trend.

2.2.a. Changes in cross-border banking flows

The crisis came with an unprecedented collapse and shifts in the structure of capital flows in general and cross-border bank lending in particular (see Figure 1 ). Contrary to past episodes, all types of countries were affected in the aftermath of the Lehman Brothers collapse (see De Haas and van Horen, 2012 , 2013 ), although emerging economies experienced a shorter-lived retrenchment in the capital inflows than advanced economies did, as shown by Milesi-Ferretti and Tille (2011) and Lane and Milesi-Ferretti (2012) . The subsequent euro crisis put further strain on that region’s banks, and intraregional private capital flows dropped sharply (see Bologna and Caccavaio, 2013 ; Laeven and Tressel, 2013 ). The collapse in bank flows among advanced countries and the fragmentation within the euro zone was driven by European and, to a lesser extent, American banks and occurred for three reasons. First and foremost, markets and regulators wanted banks to restore their balance sheets and profitability. Second, banks cut back as demand for external financing abroad was less, and sovereign and other risks increased. And, third, over time, banks had to meet tougher regulations, including stiffer capital and liquidity requirements and other new rules, and some faced restrictions on moving capital and funds across borders. 3

Evidence, some rigorous, other more anecdotal, suggests that all three—supply, demand, and regulatory factors—drove the decline in cross-border banking. In terms of supply, i.e., lender banks’ balance sheet deterioration (e.g., capital shortfalls and liquidity strains) and a reshaping of global banking system, supportive evidence is that the cutbacks in cross-border banking claims varied greatly across lenders. In terms of demand, i.e., a weakening of demand among borrowers and increased default and country risks, supportive evidence is that the cutbacks greatly varied across borrowers. And in terms of regulatory changes, many—especially bankers, of course—mention the many new rules, including Basel III, new liquidity requirements (Liquidity Coverage Ratio [LCR] and Net Stable Funding Ratio [NSFR]), macroprudential policies, and other regulatory changes, as well as the more common stress tests, overall increased regulatory governance uncertainty, and various forms of home bias as restricting cross-border activities.

While identifying the relative importance of each of these drivers is hard, some analyses do provide insights. Observing bilateral changes allows for separating demand (borrower country) from supply (banking system) factors. Specifically, since at a given point in time (say, right after a shock) banking systems from various lender countries face similar demand from a given borrower country, relative differences in changes in bilateral lending likely reflect, except for specific lender–borrower relationships, supply-side differences. This identification strategy has been used first by Khwaja and Mian (2008) and subsequently by many others ( Cetorelli and Goldberg, 2011 ; Popov and Udell, 2012 ; Kalemli-Ozcan, Papaioannou, and Peydró, 2013 ; Minoiu and Reyes, 2013 ; Cerutti, 2015 ; Cerutti, Hale, and Minoiu, 2015 ). 4

Cerutti and Claessens (2017) use this method to tease out supply versus “frictions” as drivers of flows during the period following the Lehman bankruptcy (see also Van Rijckeghem and Weder di Mauro, 2014 ). They show that banks’ cutbacks were driven by their (perceived) capital at risk. Interestingly, market indicators of vulnerabilities were less important for European banks and more so for US and Asian banks, suggesting market or regulatory disciplines varied in forcing banks to deleverage. And accounting supply variables were not significant in predicting deleveraging, sometimes even providing “wrong” signals. Furthermore, for affiliates’ lending, capital at risk in headquarter (HQ) banks was not as important, suggesting some insulation from shocks, or increases in internal market or regulatory frictions. Still, a systemic crisis at home did trigger declines in affiliates’ claims as well, a form of home bias following the often massive public support. Also, banks decreased lending to high exposures and cut back more to “farther” countries.

Other evidence suggests that changes in borrowing country demand and country risks played large roles, notably within the euro area. Bologna and Caccavaio (2013) find increased borrowing country and sovereign risks to be the main determinants of banks’ retrenchments within the zone, especially after 2010, when the euro crisis peaked. Also, Laeven and Tressel (2013) show that host sovereign and bank credit default swap (CDS) spreads explain a large share of the decline in intra-euro-area claims during 2010:Q1–2012:Q2. Other evidence finds that the degree of deleveraging was affected by the relative reliance on cross-border credit versus affiliates’ lending. For many foreign banks, local funding sources proved to be relatively stable during the GFC. Reflecting this stability, the relative reliance on foreign versus domestic funds predicted to some degree which countries experienced sudden withdrawals after the financial turmoil started in 2007 ( Cerutti, 2015 ) and which countries were at risk of banking outflows during the European crisis in 2010 ( Cerutti, Claessens, and McGuire, 2014 ).

2.2.b. Changes in foreign bank presence

There have been considerable shifts in foreign bank presence since the GFC ( Claessens and van Horen, 2015 ). While the number of foreign banks exiting markets remained more or less the same, there was much less entry after the crisis: only about one-fifth as many foreign banks entered compared with the peak year, 2007, just before the crisis. As the number of exits was similar, net entry became negative for the first time since 1995, i.e., there was some retrenchment ( Figure 5 ). As the number of domestic banks declined as well, the overall market share of foreign banks in numbers remained at about 34% at the end of 2013 (see Figure 2 ). The asset share declined, however, as domestic banks overall grew their balance sheets faster than foreign banks did, in part as governments encouraged local (government-owned) banks to continue to lend after the GFC, while many parent banks saw their balance sheets impaired. Yet foreign banks still accounted for some 11% of global bank assets as of the end of 2013, down only slightly from a peak of 13% in 2007.

Entry and exit of foreign banks, 1995–2013.

Note: As the database starts in 1995, the number of foreign banks that exited the market in that year cannot be determined.

These aggregate trends hide some important differences—both among host and, even more so, among home countries—and reflect shifts in global economic and financial powers. While for fifty-nine host countries foreign bank presence declined, for forty-five countries it actually increased ( Figure 6 ). Although the number of foreign banks declined somewhat, much activity has been in the intensive margin, as some banks were sold to other foreign parents. Using the bilateral changes, which were large ( Figure 7 ), analysis by Claessens and van Horen (2015) reveals a number of factors behind these changes. For one, banks more likely completely pull out when their home country experiences a crisis, especially when it is a euro-zone country. A systemic crisis in the host country does not affect exit, which could reflect opposing forces. Foreign banks may be willing to support their subsidiaries when the host country is in crisis (and the home country is not). But a host crisis makes for less profitable opportunities and therefore could increase exits. Overall, these effects seem to have balanced each other out. Competition from other foreign banks, from the same or other home countries, does not seem to play a significant role in a bank’s decision to exit. Individual bank characteristics do matter, however. Notably, banks that have smaller market shares and were more recently established more likely exit. And banks from home countries with a crisis more likely withdraw from markets more distant and less important as trading partners. Also, (exit) decisions tend to be more strategic and somewhat more driven by euro-zone factors in the later (2010–2012) than in the early part (2007–2010) of the period.

Change in the share of host country level of foreign assets, 2007–2013.

Notes : Only banks that have asset information for both years are included. Banks that were only active in 2007 or 2013 are also included if asset information is available for the year the bank is active. Countries in which less than 60% of the banks qualify are excluded from the sample altogether.

Changes in bilateral shares of foreign banks assets, 2007–2013.

Notes: Only banks that have asset information for both years are included. Banks that were only active in 2007 or 2012 are also included if asset information is available for the year the bank is active. Countries in which less than 50% of the banks qualify are excluded from the sample altogether. Only host countries and home–host pairs with at least one foreign bank active in 2007 and 2012 are included.

Examining the drivers of changes in individual banks’ balance sheets, Claessens and van Horen (2015) find that banks from countries hit by a systemic crisis at home expanded their foreign banks’ assets less, controlling for general asset growth in the respective host market. Foreign banks in euro-zone host countries reduced their assets less than local banks did, suggesting that they acted there as sources of stability. While more recent entrants and banks with small foreign presence before the crisis grew their balance sheets more, distant foreign banks had lower asset growth. In terms of entry, fewer banks entered from home and in host countries facing a systemic crisis and from and in euro-zone countries. Entries were greater where the (bilateral) presence of foreign banks was already large and where such banks were closer to, had more trade links with, and experienced faster growing trade with the banks’ home countries.

Many of these changes relate (again) to the problems banks in many advanced countries faced following the GFC. But they also relate to the growing importance of banks from emerging markets and developing countries, reflecting trend changes in global banking. Notably, emerging markets and developing countries continued their foreign bank expansion, representing close to 60% of the new entries. Indeed, while banks from OECD countries tend to drive exits, banks from non-OECD countries tend to drive entries. Although bank ownership by OECD countries as of the end of 2013 still represented some 89% of foreign bank assets globally, this share was some 6 percentage points below that before the GFC, mostly on account of a retrenchment by crisis-affected Western European banks. As a result, the global banking system now encompasses a larger variety of players. And foreign bank presence, already regionally concentrated, has become even more regional, with the average intraregional share increasing by some 4 percentage points, largely on account of emerging markets and developing countries, including them buying banks previously owned by OECD countries.

2.2.c. Changes in interactions between banking flows and foreign bank presence

Following the GFC, developments in cross-border claims have varied from those in foreign bank presence and related local lending, as there were sharper cutbacks in cross-border than in foreign banks’ local lending ( Figure 8 ). This result maintains when controlling for the behavior of general domestic credit, supporting the notion that foreign bank presence has been a relative source of stability for most markets and cross-border lending being more procyclical. Also, the entry by banks from emerging markets and developing countries with relatively stronger balance sheets and greater willingness to expand has mitigated declines in local lending in some markets. At the same time, how local and cross-border banking lending has changed at the country level varies much. Besides home and host developing countries’ characteristics, and strategic choices by banks as to which borrowers to prioritize, (changes in) internal market and regulatory frictions, including limits to movements of capital, underlie differences.

Growth rates of cross-border and local claims, 2007–2012.

Notes : The chart shows growth rates over 2007–2012 in local lending of all foreign banks and of foreign banks from OECD home countries, and in cross-border lending. Local lending data are from Bankscope. Cross-border lending is based on BIS consolidated banking statistics on an ultimate risk basis; only lending by OECD reporting countries is included.

Source : BIS IBS and Claessens and van Horen (2015) .

A test of the importance of various factors can be constructed by looking at whether cutbacks differed between direct cross-border and affiliates’ claims in the face of shocks to home banking systems, assuming demand and risk at the host country level was similar for both forms. 5 Several scenarios can occur. In one scenario, internal capital markets are unconstrained and, accordingly, transmit shocks equally across all parts of the banking group, leading both forms of lending to decline (proportionally). In another scenario with some internal frictions and “ring fencing,” including forms of capital or banking controls, a supply shock to the parent bank triggers a larger reduction in direct cross-border lending than in affiliates’ lending, as HQ banks cannot tap into the liquidity and capital of the affiliates. In another scenario, also with limits on moving capital and funds internally, banks actually increase affiliates’ lending as a way of bypassing limits on moving funds to the parent and then lending from there. In practice, any of these scenarios (or combinations thereof) may prevail for individual banks in specific home–host pairs. Analyzing variations among bilateral (source-destination) deleveraging after the Lehman shock, 2008:Q2–2009:Q2, Cerutti and Claessens (2017) find that direct cross-border and local affiliates’ lending differs by the size of shocks to lender country banking systems. Specifically, more vulnerable home-banking systems saw less substitution between the two, suggesting some unwillingness or inability to engage in intrabanking systems’ transfers due to (greater) internal market or regulatory frictions post-GFC.

2.2.d. Summing up

Post-GFC, there have been large reductions in cross-border bank flows, driven both by adverse supply and demand factors and by regulatory changes. With much fewer entries and the same number of exits, the number of foreign banks worldwide has declined, but not relative to the number of domestic banks, as that declined more. Cross-border lending has been more volatile than local lending activities of foreign banks. Much of these changes reflect market and regulatory forces, notably European and US banks’ retrenchment in the aftermath of their crises, and the growing role of emerging markets and developing countries in global banking. But there is also some evidence of increased internal market and regulatory barriers post-GFC.

This section reviews research on the benefits and risks of capital flows in general and of cross-border bank flows and foreign bank presence specifically. It also discusses the interactions between cross-border bank flows and foreign bank presence. It highlights how the GFC has led to much new research, with additional findings and qualifications to past findings.

3.1 The General Behavior and Effect of Capital Flows

To evaluate the effects of the (ongoing) changes in global banking on related benefits and costs, the starting point is to draw on the findings of the (extensive) literature on financial globalization and to consider how it has evolved over time. It was clear even before the GFC that financial globalization has both benefits and risks. Conceptually, the possible benefits are, foremost, that capital is allocated globally more efficiently and that risk-sharing is enhanced ( Kose et al. , 2009 , 2010 ; review the extensive academic literature written before 2008; for a related policy-based review, see IMF, 2007 ). External financing may increase with a more open capital account, and domestic resource allocation may be improved with the importing of better know-how and skills and improved access to specialized technology, such as for trade finance. Pressures from foreign capital may discipline policymakers’ macroeconomic and financial management, while entry of foreign financial institutions may help with enhancing competition and upgrading of regulation and supervision. These developments may all lead, directly and indirectly, to more capital and financing, greater allocative efficiency, and thereby higher economic growth. And in terms of economic and financial stability, risks can be exported and shared more efficiently.

There were always reasons for caution, however. Borrowing from abroad comes with its own specific risks, such as increased foreign exchange exposures and other mismatches. To the extent that global finance is more procyclical than domestic finance, the risk of (bad) booms followed by busts can increase. A greater role of foreign financial markets and institutions can lead to more cherry-picking, reduce the franchise value of domestic players, and possibly adversely affect overall local credit extension. And (systemic) risks can be imported, as when foreign banks hit by shocks at home (have to) cut back on lending to and withdraw from markets.

The view before the crisis was that the balance between the benefits and risks of capital flows was favorable in general and more so if (or provided that) the country met some “thresholds.” Notably, the literature highlighted the following factors: good macroeconomic management, a well-developed and sound institutional environment, trade openness, and a relatively large financial sector. For countries above some thresholds, financial globalization was generally thought to reduce volatility and the risk of crises. For countries below, typically emerging markets, the balance was considered more ambiguous. It was increasingly acknowledged that these countries both were not necessarily going to receive more external financing (the “Lucas paradox”) and could import financial volatility, as they generally did not meet thresholds. 6

This distinction did not prove valid before the GFC, when capital flows did not necessarily add to economic growth for countries that were otherwise well developed, and during the GFC, when capital flows were very volatile for all types of countries. Subsequently, research has focused more on understanding the factors behind the low growth effect of large capital inflows, emphasizing the misallocation toward non-tradables, notably real estate (e.g., Benigno, Converse, and Fornaro, 2015 ), and within the manufacturing sector (see, e.g., Gopinath et al. [2015] for the case of southern Europe). And in terms of capital flow volatility, the literature now emphasizes, besides differences between gross and net flows and variations in types and destinations, the exact sources of financing (investor bases) as important in affecting volatility. Raddatz and Schmukler (2012) and Puy (2016) document that international fund flows—in particular, to and from emerging markets—tend to be highly procyclical with financial conditions at home and often independent of borrowing countries’ fundamentals. Also, Jotikasthira, Lundblad, and Ramadorai (2012) find that funding shocks in funds domiciles can translate into fire sales (and purchases) for countries included in global mutual funds’ portfolios—in particular, emerging markets. Overall, while before the GFC the effect of capital flows on the real economy and financial stability was already realized to be complex, events since then have added some further caveats (for further details, see IMF, 2011 , 2013a ).

3.2 The Behavior and Effect of Cross-Border Banking Flows

One aspect of bank flows highlighted for some time has been that its volatility can be higher than that of other flows. Early on, Claessens, Dooley, and Warner (1995) found that capital flow “labels” do not closely correspond to time-series properties such as volatility or persistence, and they questioned the common presumption that short-term bank flows are the more volatile type. However, several later studies ( Levchenko and Mauro, 2007 ; IMF, 2011 ) suggest that banking flows tend to be more volatile than other flows (FDI flows are the least volatile) and have low persistence (albeit more than portfolio debt flows), especially for emerging markets. Relatedly, higher procyclicality of debt flows with respect to domestic financial and economic aggregates has been documented, notably for emerging markets and developing countries. 7

The GFC has confirmed and has also disproven some of these properties. Advanced countries with generally better macroeconomic, financial systems, and institutional environments experienced as much or more volatility in (gross and net) bank flows as emerging markets did. And the “flight home” during the GFC was general as banks retreated back to major investor and creditor countries, even though many of these countries were themselves subject to systemic financial crises ( De Haas and van Horen, 2012 , 2013 ; Giannetti and Laeven, 2012 ).

Yet flows to banking systems, especially those from other banks, were the more volatile and declined post-GFC the most, more so than flows to non-financial corporations. This greater volatility of bank flows has since, in part, been related to the fact that banks are more affected by global financial and monetary policy conditions. Bruno and Shin (2015a , 2015b ) document how internationally active banks expand and contract their cross-border claims in part in response to monetary policy and banking system conditions in advanced countries (see also Shin, 2012 ). Relatedly, also taking the recipient country perspective, Cerutti, Claessens, and Puy (2015) show that capital flows for those emerging markets relying more on international banks are more sensitive to global factors.

3.3 The Behavior and Effect of Foreign Banks

While not always done, the starting point for an empirical analysis of the role of foreign banks and whether and how they affect domestic financial systems and economies is comparing their behavior with that of domestic banks. Do foreign banks and domestic banks behave in the same or different ways? Are foreign banks’ activities complements to or substitutes for those of domestic banks? Do they provide the same or different types of financial intermediation services? From there follow questions about how the behavior of foreign banks affects that of domestic banks and the overall domestic financial system. Do they encourage more efficient banking systems? How do they affect the real economy, i.e., how does foreign bank presence affect access to finance for firms and households and in turn their performance? And what are the links between foreign bank presence and financial stability, including financial booms and busts and volatility in capital flows? I will review the evidence on each of these issues next.

3.3.a. Behavior of foreign banks compared with that of domestic banks

Foreign banks differ from domestic banks in terms of business models, as much anecdotal evidence suggests, and balance sheets (solvency, liquidity risks, and otherwise). How pervasive these differences are and how they vary by the country in which the bank operates, and possibly by the bank’s home country, are less clear. Using financial statements, Claessens and van Horen (2012) show that in advanced countries, foreign banks are less involved in traditional forms of financial intermediation (i.e., deposit-taking and lending) and more involved in investment banking and other, less traditional forms. In emerging markets, the reverse is true: foreign banks tend to be more active in lending. In terms of capital and liquidity, foreign banks in general tend to be less leveraged and have higher capital and liquidity ratios than domestic banks do, i.e., they are more conservative than domestic banks are. While evidence that globally active banks have special skills is mostly anecdotal (see Levine [1996] for some examples), Claessens, Hassib, and van Horen (2016) show that foreign banks facilitate trade over and beyond what domestic banks do.

The differences in terms of asset mixes, funding structures, and activities mean the performance of foreign banks differs from that of domestic banks, with these differences also varying by host country and by home country and bank characteristics. For the USA, studies find that foreign-owned banks perform significantly worse than domestic US banks do ( Goldberg and Grosse, 1991 ). DeYoung and Nolle (1996) and Mahajan, Rangan, and Zardkoohi (1996) also find that foreign banks underperform domestic banks in high-income countries. Other studies, however, find that foreign banks perform better than or not differently from domestic banks. For emerging markets and developing countries, findings vary.

Claessens and van Horen (2012) , studying the performance of foreign relative to domestic banks in seventy-four countries from 1999 to 2006, find that foreign banks, on average, outperform domestic banks, with profitability some 0.3 percentage point higher than the mean profitability of 1.6%. It can take some time, though, for foreign banks to outperform domestic banks. As Correa (2009) shows, banks acquired by foreigners do not perform better than domestic banks in the first 2 years after the acquisition. Foreign banks are especially profitable in developing countries and less so in emerging markets. Also, in countries where the cost of contract enforcement is relatively high or the availability of credit information low, foreign banks are more profitable. Profitability is especially high in countries where foreign banks do not dominate, not where they dominate, consistent with other studies (e.g., Claessens and Lee, 2003 ). Foreign banks from high-income countries also perform better, suggesting know-how and access to capital matter, and when their home country has the same language and similar regulation as the host country or is close by, suggesting closeness eases the collection of soft information and its internal transmission.

3.3.b. Effect of foreign banks on local banking markets

The general consensus pre-GFC was that the effect of foreign banks on host countries was, overall, mostly positive, with multiple factors driving these benefits (see review papers by Levine, 1996 ; Clarke et al. , 2003 ; Claessens, 2006 ; Chopra, 2007 ). Foreign bank presence can mean additional external financing, especially for activities that need global networks and specialized skills, such as trade finance or investment banking-type activities, and for specific types of firms, such as large, multinational corporations. Foreign bank presence can enhance competition, leading to lower rents, higher efficiency, and lower intermediation costs. As foreign banks bring with them improvements in products and superior technology and know-how, spillovers to domestic banks can occur, leading to better overall financial intermediation. Foreign banks can also pressure governments to improve their regulation and supervision, increase transparency, and more generally catalyze domestic reforms ( Mishkin, 2007 ), including by helping reduce the often close and perverse links between local banks and politicians. All of these improvements can in turn lead to increases in access to financing for and improved performance of final borrowers.

Many empirical studies have documented some of these effects before the GFC, although specific evidence for spillovers is scarce. Early studies (e.g., Claessens, Demirguc-Kunt, and Huizinga, 2001 ; Mian, 2003 ; Berger et al. , 2005 ) found that greater foreign bank presence coincides with lower overall costs of domestic financial intermediation (measured by, among other metrics, margins, spreads, and overheads). In terms of competition, Claessens and Laeven (2004) show that it does not require a large foreign bank presence; what is more important is that the local banking system is contestable, i.e., without entry restrictions. However, direct and spillover effects can depend on conditions in the host country. Limited general development and entry barriers seem to hinder foreign banks’ effectiveness ( Demirguc-Kunt, Laeven, and Levine, 2004 ; Garcia-Herrero and Martinez Peria, 2007 ). Also, with more limited presence, fewer spillovers arise, suggesting a threshold effect ( Claessens and Lee, 2003 ).

Evidence also exists of better-quality lending with greater foreign bank presence, e.g., lower loan-loss provisioning and better-performing borrowers ( Martinez Peria and Mody, 2004 ). In terms of access to finance, however, results vary by bank and firm characteristics. Beck, Demirguc-Kunt, and Maksimovic (2004) and Berger et al. (2004) conclude that a larger foreign presence leads to a greater availability of credit for SMEs. Clarke, Cull, and Martinez Peria (2002) find that foreign bank entry improves financing conditions for enterprises of all sizes, although larger firms benefit more. Brown et al. (2011) find evidence of greater access to finance for more transparent firms only. Giannetti and Ongena (2012) show that greater foreign bank presence increases the probability that all types of firms get access to bank loans, even though large and foreign firms more likely have a relationship with a foreign bank and small firms with private domestic banks.

One concern has been that foreign banks would cherry-pick borrowers, negatively affecting overall private credit as they worsen the credit pool remaining for domestic banks, especially so in countries where relationship lending is important. Indeed, Detragiache, Gupta, and Tressel (2008) find that in low-income countries, where relationship lending is more important, greater foreign bank presence is associated with less overall credit. However, Cull and Martinez Peria (2011) show that this effect disappears, or even reverses, once crisis-induced acquisition of (distressed) banks by foreigners is accounted for. And while Claessens and van Horen (2014b ), as in Detragiache, Gupta, and Tressel (2008) , find a negative effect of foreign bank presence on private credit for developing countries, they find no adverse effects in emerging markets and high-income countries. The negative effect also occurs only when foreign banks have a limited market share, enforcing contracts is costly, and credit information is limited. These results suggest that certain market characteristics, not the general income level, make foreign banks cherry-pick customers.

Evidence also shows that banks’ home conditions matter for lending in foreign markets, including that regulations can make banks look for risks abroad. Ongena, Popov, and Udell (2013) show that lower barriers to entry, tighter restrictions on bank activities, and higher minimum capital requirements at home are associated with lower bank lending standards abroad. They also find stronger effects when banks are less efficiently supervised at home, which are not offset by host country regulation. And Houston, Lin, and Ma (2012) find that banks transfer loanable funds to markets with fewer regulations when there is an effort by domestic regulators’ ability to limit bank risk-taking. Also, more internationalized economies and those with a larger foreign (bank) presence, most typically emerging markets, are more affected by global monetary conditions ( Cetorelli and Goldberg, 2012c ). Evidence for Mexico shows that the monetary policy stances in the USA, UK, and euro zone affect the local credit supply of the foreign affiliates of parent banks from those countries ( Morais, Peydro, and Ruiz Ortega, 2015 ).

In summary, while, in general, foreign banks have many benefits for host economies, these can depend on certain conditions. Lower general development and larger barriers, including a weaker institutional environment, can hinder positive effects. There can be a threshold effect, where, with limited entry, fewer spillovers arise. In contrast, greater presence and a larger footprint (more branches) mean more likely greater access to external financing, including for SMEs. Relatedly, while foreign banks can cherry-pick borrowers and even lower overall credit, this practice mainly arises in low-income countries and not so when banks are large in the market and from home countries that are close. And while risks can be imported from abroad, healthier (parent) banks from “better” home countries are associated with more and higher-quality credit.

3.3.c. Effect of foreign banks on financial stability

The role of foreign banks with respect to financial stability was high on the policy and research agenda before the GFC, and it has been even more so since then. As a start, it is important to realize that international risk-sharing has several sides. As theoretical models point out, multinational banks can mitigate local financial shocks, transmit foreign financial shocks, and exacerbate shocks to the real economy. Foreign banks can help with financial stability in that they can export risks away from the host country, with the risk-sharing coming about in part as they have easier access to global funds and capital in times of stress in the local economy or financial system. But foreign banks can also import shocks to the host market, as has become clear with the GFC.

Models acknowledge these effects and make clear that effects can vary by bank and shock, with much depending on the functioning of banking groups’ internal capital markets. In Kerl and Niepmann (2014) , banks choose between lending internationally intrabank, interbank, and to foreign firms. Given, among other factors, impediments to foreign bank operations, this means responses to shocks will vary among banks, which is consistent with German bank-level data they review. Blas and Russ (2013) contrast the competitive effects from cross-border bank takeovers with those of cross-border lending, and find that the former not to be adding to competitive pressures, but the latter reducing markups and interest rates. And the behavior of banks will differ depending on whether they are faced with a real-economic shock or a financial shock. In Morgan, Rime, and Strahan’s (2004 ) two-country model, for example, cross-border banking integration increases (decreases) output co-movement after asymmetric shocks to the financial (real) sector. Using a general-equilibrium model of international business cycles with multinational banks, Kalemli-Ozcan, Papaioannou, and Perri (2012) come to a similar conclusion (see also Kollman, Enders, and Müller, 2011 ).

In terms of empirics, several papers have highlighted that foreign banks indeed can enhance financial stability when there is stress or a crisis in the host country as parent banks support their subsidiaries. This has happened in many cases, notably in the fall of 2008, when foreign banks supported their international operation in Central and Eastern Europe ( De Haas and van Lelyveld, 2014 ; EBRD, 2015 ). In Eastern Europe, as shocks coming from abroad were large and many firms became more credit constrained, spillovers were also the focus of policy. The Vienna Initiative (VI) specifically aimed to address this issue, with benefits ( De Haas et al. [2015] show that banks participating in the VI were relatively stable lenders; for further details, see EBRD, EIB, European Commission, IMF, and the World Bank [2015] ).

But also earlier, foreign banks were a source of financial stability as they reallocated funds and liquidity across locations in response to host country crises. This result has been shown directly for US banks and indirectly by investigating the performance of foreign affiliates and domestic banks. Studying episodes in (mainly) emerging markets and developing countries, Crystal, Dages, and Goldberg (2001) and De Haas and van Lelyveld (2006) show that thanks to the support of their parent banks, foreign affiliates did not need to rein in credit during a crisis in the host country, while domestic banks did contract their lending. But the degree of such benefits can vary. De Haas and van Lelyveld (2010) compare foreign banks with large domestic banks and find that subsidiaries were more stable lenders during the crisis in case other subsidiaries in the same group were more liquid or held more capital. They take this finding as evidence of multinational banks operating an internal capital market through which they reallocate liquidity and capital in response to shocks.

While findings on exporting risks are thus favorable in general, the flip side of risk-sharing is that when faced with shocks at home, foreign banks might withdraw from cross-border banking activities and redirect lending to home. Shocks (capital or funding) to parent banks or their home markets more generally can be transmitted to foreign affiliates, negatively affecting their local lending and activities. Also, parents may repatriate capital and liquidity from their foreign affiliates, which in turn can negatively affect their supply of credit in the host market. The seminal studies of Peek and Rosengren (1997 , 2000 ) show indeed that (funding) shocks to (Japanese) parent banks were transmitted to their foreign (US) branches, with negative consequences for their lending in the USA and with real economic costs. Effects can vary, too. Schnabl (2012) shows that while the negative liquidity shock resulting from the Russian default in the late 1990s led international banks to reduce lending to both domestic and foreign-owned Peruvian banks, which in turn reduced their lending to Peruvian firms, parent banks continued to support their own Peruvian affiliates.

Studying recent crises, analyses have also found that foreign banks can import risks, with studies suggesting that at the height of the crisis, global banks transmitted shocks across borders through their affiliates. Cetorelli and Goldberg (2011) , in their analysis using BIS data for globally active banks in seventeen source countries, document spillovers to emerging markets but also transmission of bank distress to firms’ access to external financing. Cetorelli and Goldberg (2012a ) show that foreign banks with a high exposure to the subprime crisis transmitted stress into US markets by reducing net internal funds available for their US branches and then having these branches engage in less lending. These transmissions have had real economic consequences. Popov and Udell (2012) show that if banks in the vicinity of the firm were experiencing distress at the onset of the GFC, the likelihood of a firm being credit constrained increased, with this transmission also taking place when shocks occurred to the balance sheet of the parents of subsidiaries.

As for other cases, evidence for the GFC shows differences in shock transmissions. De Haas and van Lelyveld (2014) do not always find evidence of an active internal capital market, whereas Cetorelli and Goldberg (2012b) show that US banks adjust their interoffice liquidity and claims in response to variations in domestic liquidity (although the evidence is not as strong after the Lehman bankruptcy). 8 Cull and Martinez Peria (2012) show that while in Central and Eastern Europe loan growth by foreign banks fell more than that of domestic private banks during the crisis, foreign banks in Latin America did not contract their loans at a faster pace. The distinction seems to be driven by the fact that these banks were mostly funded through domestic deposits with most lending in domestic currency, in part forced by host regulatory requirements, which allowed them to maintain lending even when their parent banks were hit by funding shocks. In contrast, in Central and Eastern Europe, subsidiaries were mostly funded through wholesale funding, including from internal capital markets, which was more volatile.

Experiences thus do not easily generalize, and, as other recent studies suggest, with respect to financial stability, similar to access to finance, one cannot look at foreign subsidiaries as a homogeneous group. Claessens and van Horen (2013) investigate how bank and other (country) differences influenced the degree to which shocks to parent banks during the GFC affected local lending. Considering all countries analyzed (some 100), the authors found that foreign banks reduced credit by 6 percentage points more in 2009 compared with domestic banks ( Figure 9 ). This difference is large, as the mean credit growth was only 5%. However, only for developing countries and emerging markets was there a significant difference between foreign and domestic banks, but not for advanced countries. And while in countries where foreign banks hold less than 50% of domestic assets, the loan growth of foreign banks in 2009 was 7 percentage points less than that of domestic banks; when foreign banks dominate, there was no difference. They also find differences to vary more for countries where foreign banks are distant and close.

Credit growth during the GFC, foreign versus domestic banks.

Notes : The figure shows the point estimates and 5% and 95% confidence intervals of a foreign ownership dummy interacted with a dummy that is 1 if the year is 2009 in panel regressions estimated using different country samples. All regressions include several bank-level controls and bank and country-year fixed effects.

Source : Claessens and van Horen (2013) .

A large variety in banks’ responses to liquidity shocks is also found in the eleven-country case studies of how liquidity risks are transmitted from global banks to local markets, conducted under the International Banking Research Network (IBRN) and published in the IMF Economic Review (2015) . The overview paper by Buch and Goldberg (2015) summarizes the findings as follows (in its abstract, page 377): “First, liquidity conditions affecting parent banks transmit into both the domestic and foreign lending of these banks. Second, the ex-ante balance sheet composition of banks and banks’ business models influence their responses to liquidity risk. No single balance sheet characteristic consistently plays a role in liquidity risk transmission. Third, internal liquidity management within multinational banks can alter the domestic lending effects of liquidity risk. Fourth, the availability of official sector liquidity tends to reduce the adverse consequences of private liquidity conditions for bank lending during stress periods and to weaken the impact of bank balance sheet constraints.”

While studies indicate that it is important to account for heterogeneity across banks and (home and host) countries when examining foreign banks’ (crisis) behavior, a recurrent finding of what matters for the stability of lending is having access to local, stable deposits (as Correa, Goldberg, and Rice [2015] show, reliance on volatile wholesale deposits makes branches (or subsidiaries) of foreign banks, like domestic banks, subject to runs with adverse financial stability implications). In principle, the relationship can go two ways. On the one hand, foreign banks that are large local, stable deposit-takers might be less affected by shocks to their parent’s balance sheets, as they have their own funding markets. On the other hand, parent banks faced with funding shocks might be inclined to transfer funds from those subsidiaries more active in local deposit-taking. Cetorelli and Goldberg (2011) show that while on a host country level there is no difference with respect to having access to local deposits, at the bank level, foreign banks that have a strong deposit base reduce credit significantly less than other banks. This finding shows again the importance of stable local funding structures for credit provisioning when parent banks are hit by a shock.

These findings put into perspective other recent studies and confirm why in Eastern Europe, foreign banks contracted more, but not in Latin America. They also concur with Ongena, Peydro, and van Horen’s (2015) finding that in Eastern Europe, foreign banks reduced lending more than locally funded domestic banks, but not compared with domestic banks that had financed their pre-crisis lending by borrowing from international capital markets. It is also consistent with De Haas and Van Horen’s (2012 , 2013 ) finding that, when faced with a funding shock, banks reduce their cross-border lending but are more likely to stay committed to countries in which they have a subsidiary, especially in countries with weak institutions.

In summary, foreign ownership can help with a host country’s financial stability since parents can and do support their affiliates in times of stress, especially when they made a commitment in terms of a (large) brick-and-mortar presence. Furthermore, when present locally, global banks’ cross-border lending is more stable during a host country crisis than when banks are not present. While when parents are faced with a funding shock this can adversely affect the lending of their subsidiaries, whether this transmission indeed takes place depends importantly on local conditions and the business model of the subsidiary. Foreign banks are less likely to contribute to financial stability (1) in emerging markets and developing countries, (2) where they capture less of the domestic market, and (3) when they are less reliant on local funding. Collectively, these results point again to the importance of considering heterogeneity when analyzing foreign banks’ effect.

3.4 The Effect of Foreign Bank Presence on Overall Foreign Financing and Domestic Credit

Foreign bank presence and cross-border bank flows can interact to affect overall external financing and domestic credit conditions. At the microlevel, interactions will reflect the operations of internal capital markets between parents and local affiliates where liquidity and capital are allocated in response to relative demand and supply, and considering regulatory frameworks. But local affiliates may also screen and monitor (new) clients, yet (large) loans may be booked on the parent’s balance sheet (e.g., to address large-exposure limits and other regulations that apply to the subsidiaries). There can also be macroeconomic interactions. Greater local presence can, on the one hand, increase current account deficits but can also, on the other hand, lead to more domestic savings and less net capital flows. Presence could affect the composition of capital flows and lead to more “good” or “bad” forms of foreign financing, i.e., less or more (bank) debt. More generally, presence could affect the degree of procyclicality and the occurrence of domestic (bad) booms and busts. Many of these questions have not been analyzed, in part because one needs to consider many other policies—including monetary and fiscal policies, interest rate differentials and exchange rate movements, and macroprudential and capital flow management policies—as those importantly can drive and affect both capital flows and domestic credit developments.

While there can be many (common) drivers, in practice, there appears to be little relation between developments in foreign bank local lending and aggregate cross-border capital flows. Consider, for example, foreign bank presence in and capital flows to Central and Eastern Europe and within the euro zone. Before the GFC, some countries—e.g., the periphery countries in the euro zone—had small foreign bank presence and large current account deficits, while others, such as in Central and Eastern Europe, had large foreign bank presence and large current account deficits ( IMF, 2013b ). The form of financial integration can matter, though, for the type of capital flows. Before the crisis, as Laeven and Tressel (2013) show, emerging European countries, with large foreign bank presence and large cross-border intragroup capital flows, experienced a significantly faster buildup of foreign liabilities than other European Union (EU) countries did. Also, in other cases, there were perverse links between foreign bank presence and the type of capital flows, as argued by Shin (2010) for the case of Korea, where branches of foreign banks engaged in large-scale carry-trades borrowing short-term, making interest rates and exchange rates more volatile. As such, foreign bank presence can alter cross-border bank flows.

There is also some evidence that (certain types of) foreign banks can affect general local credit booms. Analyzing pre-GFC credit booms, Claessens and van Horen (2016) find overall foreign presence to have little relationship with the size of domestic booms. Controlling for aggregate domestic credit expansion and investigating the role of individual banks show that foreign banks add more than domestic banks do to local booms, but mainly because they have better financial conditions. Foreign banks do import in part a banking boom at home to the host country, however, and add more to a boom if from less well-regulated systems. Supportive of this finding, Mehigan (2015) finds that foreign banks from home countries with lower capital requirements had significantly higher local loan growth in the years leading up to the GFC.

At the same time, foreign bank presence can help stabilize overall capital flows after a shock. Laeven and Tressel (2013) show that after the GFC, emerging European countries, on average, experienced a slower reversal in capital flows, accounting for other determinants and home country factors (but not formally for internal market frictions or regulatory measures). In the euro zone itself, in contrast, fragmentation increased, with lending interest rates diverging for an extended period, largely because of cutbacks in cross-border flows. While foreign banks also cut back on lending, their presence within the euro zone was relatively limited, much less than in emerging Europe, and thus not a stabilizing force. Heterogeneity in the form of financial integration (e.g., high local presence, only partially funded by intragroup flows, compared with large cross-border flows between unrelated lenders and borrowers) thus matters both for global banks’ role in adding to local vulnerabilities and for their role in post-crisis busts.

3.5 Summary and Implications

The benefits and risks of global banking depend on a number of factors, including the structure of global and local banking systems, which makes assessing the overall benefits of foreign banks complex. As the global banking landscape is changing following the GFC, the nature of potential gains and risks going forward are also altering. Understanding both the drivers of reshaping and what these may mean for the functioning of the global banking system is thus important from economic, financial, and policy angles as well as for guiding future research. In the meantime, there are some issues, on which policy decisions are currently being made. I review these issues and try to clarify them in light of existing research.

The agenda for reforming the international financial architecture, broadly understood as the mechanisms that facilitate the smooth and efficient flow of financial services and capital across countries and ensure global financial stability, is large (see Obstfeld [2013] and Eichengreen [2016] for reviews). It involves issues such as redesigning the global safety net, possibly including a larger International Monetary Fund with greater emergency financing facilities and greater use of (bilateral) central bank swap lines; revisiting the degree of capital account liberalization and, relatedly, the use of capital flow management and macroprudential policies; and possibly designing mechanisms aimed at addressing international spillovers from (unconventional) monetary policy. The focus of this section is on the much narrower topics of the rules for trade in banking services and how to deal with global banks in terms of regulation, supervision, and resolution. Even then, many issues arise, and only a few have received much analysis. One of the principal lessons of the GFC is that banks are “global in life, but national in death” (as per the Bank of England Governor Mervyn King). Many governments had to support their banks (and banking systems more generally), even when losses were largely due to their international operations. Moreover, some national actions (or a lack thereof) negatively affected (banking systems of) other countries. I will therefore focus mostly on changes being made to improve the regulation, supervision, and resolution of internationally active banks.

4.1 Changes in Openness and Other Barriers to Trade in Financial Services

4.1.a. trade in financial services.

Although the crisis has led to a reevaluation of the risks and benefits of international banking and a tightening of domestic financial regulations, it did not discourage countries—in particular, emerging markets, usually already open and large hosts—from formally further opening up ( Claessens and Marchetti, 2013 ). In fact, there was a general further elimination of restrictions on market access and discriminatory measures (which favor domestic over foreign firms) in banking, securities, and insurance markets, as well as a consolidation of previous reform efforts. In addition, countries continued to enter preferential trade agreements, which most often also give financial institutions easier access to one another’s markets. Some fifty-two such agreements became effective from the onset of the crisis until mid-2013, two times more than between 2000 and September 2008. And although the so-called Doha Round of global trade negotiations made little progress in increasing market access and reducing barriers to trade in financial services, several liberalization initiatives have emerged over the past few years. Three of those initiatives hold the promise of further—and possibly significant—liberalization, including in financial services: the thirteen-nation Trans-Pacific Partnership; the Transatlantic Trade and Investment Partnership between the EU and the USA; and the Trade in Services Agreement, which involves twenty-one economies and the EU.

4.1.b. Other regulatory changes

While countries still opened up their markets de jure, this fact should be balanced with other, sometimes more anecdotal, evidence that rules on cross-border flows have been tightened and informal barriers have increased. Regulatory measures here include both those aiming to reduce the risks of crises and those adopted during a period of financial turmoil or crisis to reduce adverse effects. While both can affect the de facto openness of financial markets, effects can go various ways. Take the case where a country tightens its macroprudential policies by, say, increasing capital requirements. In the absence of full reciprocity by foreign authorities (i.e., if they do not also put similar restrictions on lending to the country), cross-border inflows could increase if borrowers go directly to international financial markets rather than borrow from local banks. There is evidence for this scenario. Aiyar, Calomiris, and Wieladek (2014) show that foreign bank branches increased their lending in the UK in response to tighter measures applied to local banks, a sign of cross-border competition and regulatory arbitrage. Cerutti, Claessens, and Laeven (2016) document that greater use of macroprudential policies increases the ratio of cross-border to local lending. And Akinci and Olmstead-Rumsey (2015) find leakage effects in that total credit, which includes non-resident lending, is less responsive to macroprudential policies than local credit is. Other (regulatory) characteristics of the home country likely matter here.

Spillovers can also arise outward, i.e., when institutions adjust to local restrictions by decreasing or increasing their cross-border activities. Macroprudential policies, for example, can lead domestic banks to become less active internationally. Aiyar et al. (2014) show that, because during the 2000s supervisors required UK-based banks and subsidiaries to meet higher capital requirements, local banks lent less abroad, which may or may not have been optimal. Additionally, when policies at the source country do not effectively stem risks related to outflows, recipient countries may be negatively affected. Country and financial market characteristics matter here again, as the scope for avoidance and barriers is not equal everywhere. Cerutti, Claessens, and Laeven (2016) find, for example, that leakage of macroprudential policies is larger in more developed countries, maybe as borrowers find it easier to tap alternatives. 9 But measures can also amount to restricting access to a market in case they end up weighing heavier on non-residents than on residents. For example, increased reserve requirements on foreign exchange deposits adopted for macroprudential objectives may disproportionately affect foreigners. Indeed, Bruno, Shim, and Shin (2017) document for twelve Asia-Pacific countries how changes in reserve requirements, as macroprudential measures, affect capital inflows.

In terms of regulatory actions after the GFC, there is anecdotal evidence and some research on ring-fencing. Cerutti and Schmieder (2014) and D’Hulster (2014) document additional limits on outflows and regulatory actions in a number of markets, both developed countries and emerging markets (for further details, see IMF, 2015 ). And banks that received government support may have been incentivized to reduce their international activities (more)—including in the form of selling subsidiaries. 10 Also, the EU competition policy agency asked some European banks that were intervened and received state support to divest activities and sell subsidiaries ( Boudghene and Maes, 2012 ). As such, retrenchment decisions made were driven not only by bank choices, but also by home country and supranational factors. Overall, while countries continue to further open de jure, macroprudential policies and other (regulatory) actions, including some ring-fencing, likely led to some barriers and increased home biases.

4.2 Regulations Affecting Global Banks

While global banks have many benefits, they can also introduce risks, in part as they are hard to manage and difficult for markets to discipline. Important from a global perspective is that risks can spill across borders. The cross-border vulnerabilities of global banks can be easy to ignore, especially during booms, but home–host conflicts easily arise in times of stress. And, as the GFC showed, cross-border banks are difficult to resolve. Many improvements can be and are being made in the (international) regulation and supervision of global banks to address these issues. In the end, though, as this section will argue, the fundamental issue is about resolution where choices have to be made. I will discuss and review the related literature from this perspective.

4.2.a. Management, governance, and regulations of cross-border banks

The governance of banks is arguably more challenging than that of non-financial corporations (see Laeven, 2013 , and De Haan and Vlahub, 2013 , for reviews). Banks are “special” in a number of respects: they are highly leveraged; have very diffuse debt holders; are opaque and can adjust their balance sheets quickly; and, while closely regulated, benefit from a public safety net, some in the form of deposit insurance. While differences can be overstated—and, indeed, as Laeven (2013) argues, many of the “good” corporate governance principles for non-financial corporations also apply to financial institutions—there are complications nevertheless. Relatedly, the Basel Committee on Banking Supervision ( BCBS, 2015 ) and others ( Bank of England, 2015 ) have issued guidelines on bank corporate governance that (implicitly) acknowledge these complexities and that the best policy responses are not obvious.

The internal and external corporate governance and related market discipline challenges are even larger as banks expand across borders. Managing a large systemic, cross-border bank is clearly very complicated. These banks can be very international: as of 2010, the top thirty banks had, on average, 53% of their assets and earned 56% of their income abroad. The number of subsidiaries further shows the complexities: the top thirty had, on average, close to 1,000 subsidiaries, of which 68% operated abroad and 12% in offshore financial centers (for additional information, see Claessens, Herring, and Schoenmaker, 2010 , and Carmassi and Herring, 2015 ). Logic suggests that management and governance would be even harder in these banks than in others. Challenges compound, as many of these banks are, to various degrees, “too big to fail,” i.e., they are G-SIBs. Ex ante, this means that they benefit from an implicit subsidy, which (further) distorts their shareholders’ and management’s incentives and complicates their governance. Before the GFC, the “subsidy” from expected bailouts reflected in their cost of funds was estimated to be about 45–80 basis points for G-SIBs ( IMF, 2014 ). Importantly, in spite of reforms and changes in bank behavior, this subsidy was still large (estimates vary, but Ueda and Weder di Mauro, 2013 , find it to be between 60 and 80 basis points).

In response to the GFC, many new regulations have been announced (for details, see the latest Financial Stability Board (FSB) progress report to the Group of Twenty, FSB, 2015a ), with many specifically to affect global banks. G-SIBs (as well as domestic SIBs, or D-SIBs) are being identified annually. The new Basel III capital requirements include systemic risk-varying surcharges for G-SIBs. Another important new rule requires higher loss absorbency capacity for G-SIBs (so-called Total Loss-Absorbing Capacity, or TLAC) in case of financial distress. 11 While not specifically aimed at G-SIBs, the new liquidity standards—the LCR and the NSFR—affect G-SIBs more than other banks. And there is more intense supervision in most countries, including through supervisory colleges (established for almost all G-SIBs). More is needed, however, to strengthen supervisory cooperation, which will importantly depend on progress in cross-border resolution of G-SIBs.

4.3 Resolution of Cross-Border Banks

Resolution of banks differs from that of non-financial corporations. Whereas bankruptcy and subsequent restructuring of corporations can extend over considerable periods, a timely solution is of the essence for banks, given the risks of runs by depositors and other creditors and the associated loss of value. A SIB, however, is by definition hard to resolve: its typically large size, extensive connections, and sometimes unique role in providing essential services make it hard to close or liquidate quickly and without disruptions to the rest of the system. When SIBs run into difficulties, resolution therefore often requires government support. Indeed, few SIBs are resolved quickly normally, and none in a systemic crisis. These issues get amplified for G-SIBs. During the GFC, some G-SIBs “failed” outright, and more ran into trouble in 2008–2009 than in the prior two decades. As they did, governments provided large support to them: of all the banks that ran into trouble, G-SIBs represented just 16% of the assets but received 54% of the support (see data from Laeven and Valencia, 2013 ). While ongoing reforms aim to reduce the presence and adverse effects of G-SIBs, the question of how to avoid governments being coerced into providing a bailout because of fear of creating a systemic crisis remains.

The supervisory and resolution challenges for a G-SIB relate in large part to coordination problems. A weakly supervised G-SIB and its failure pose adverse cross-border effects, but these are often ignored by authorities, as their accountability is typically national (as that is how they are organized and funded). Also, most legislations and procedures for insolvency and restructuring are national and can vary considerably. Asymmetries in domestic and international activities, both assets and liabilities, then mean that national interests can diverge, as the model of Freixas (2003) shows. For a G-SIB active in two countries but large in both, incentives are more likely to be aligned, as both countries’ supervisory agencies would want to intervene in case of financial stress. For a G-SIB large at home but small in the host country, however, conflicts are likely, as the home supervisory agency is less interested in preserving financial intermediation in the host country. When the G-SIB is systemic in the host country but not necessarily large in the home country, a case many emerging markets and developing countries face, the home country may have little interest in intervening, yet the failure can cause major havoc for the host country. These differences are exacerbated when fiscal, financial, and supervisory capacities are not commensurate with the scale and scope of activities of G-SIBs in each country. For these and other reasons, authorities face imperfect incentives and incomplete tools in dealing with G-SIBs.

Beck, Todorov, and Wagner (2013) document formally some of these biases. They find, using the CDS prices of large (mostly cross-border) banks three days before their interventions during the 2008–2009 crisis, that there were stronger incentives to intervene if their equity was owned by foreigners, and weaker incentives if assets were lent or invested abroad and deposits were owned by foreigners. Put differently, national supervisory agencies were more willing to intervene when there were adverse consequences for domestic depositors and the local economy and when the costs of doing so––by “wiping out” equity holders––were more likely to be borne by foreign owners.

While supervisors knew before the GFC that the cross-border bank resolution framework was not perfect, the expectation was that memorandums of understanding (MoUs) between supervisory agencies and so-called colleges of supervisors (designed to jointly oversee specific institutions) would suffice. This approach clearly did not work, since during the GFC there was little de facto cooperation (in part as most MoUs actually had a clause making them not binding in some extreme state of nature) and most cross-border resolutions were poor. Some examples include—besides, of course, Lehman Brothers—Dexia, Fortis, and Icelandic banks as well as American International Group, Inc., or AIG (see Claessens, Herring, and Schoenmaker [2010] and Schoenmaker [2013] , for in-depth reviews of these and other cases). In each case, resolution was, out of necessity, improvised. In some cases, it succeeded in limiting spillovers, but at substantial cost to local taxpayers. In other cases, it protected domestic interests with little regard to international spillovers. At times, the model being followed became unclear itself, e.g., as an “improvised” cooperation raised questions about how other banks might be handled. Since then, it is acknowledged that improving cross-border intervention and resolution in case of stresses is key, also to enhancing regulation and supervision beforehand.

4.3.a. Financial trilemma

The proximate reasons for these poor resolutions are multiple and (with the benefit of hindsight) understandable: limits on financial and supervisory resources, poor information, and uncertainty about what causes a G-SIB failure and what the consequences of a failure would mean for the global financial system. The deeper causes for the problems lie with the financial trilemma , first coined by Schoenmaker (2011) and subsequently used by Obstfeld (2015) and others. Conceptually, it builds on other trilemmas well known in economics, e.g., the so-called impossible trinity of capital mobility, fixed exchange rate, and independent monetary policy, as well as others that have recently been extended, especially in the context of the euro zone. 12 The financial trilemma is that three policy objectives—maintaining global financial stability, fostering cross-border financial integration, and preserving national resolution authority—do not easily fit together: any two of the three objectives can be achieved with relative ease, but achieving all three is difficult, particularly within a monetary union. The trilemma forces policymakers to make choices, which can be done using one of two corner approaches, universalism or territoriality , or an intermediate approach, the one discussed here being called modified universalism . 13 Implementing any of these approaches will be complex and has to recognize the ongoing shifts in global banking.

4.3.b. Universalism

This term, used for non-financial corporations’ bankruptcies, involves an equitable distribution of the estate, regardless of the location of the subsidiaries of the firm. Applying this model to G-SIBs would mean that the bank would be subject to a single resolution conducted by the country where the G-SIB is headquartered. While universalism creates clarity in that the home authority is in charge, it does not avoid all conflicts since asymmetries can remain. For example, when the subsidiary is systemic in the host country but the parent bank is not systemic at home, the home country may not intervene “enough” from the host’s perspective. Ideally, this model then also has unified regulation and supervision; a universally recognized mechanism for liquidity support; and the ability and willingness, if needed, to engage in cross-border burden sharing. Lacking some of these elements, conflicts can remain (for additional information, see Hüpkes, 2005 , 2016 ).

To make the universal model work will require a predetermined agreement regarding the sharing of burdens in case of losses. While various models for loss-sharing can be considered, the key is to do this ex ante rather than to improvise ex post—as generally has happened to date. Doing this ex ante has the main benefit that the countries involved have greater incentives to make sure that each makes adequate supervisory investments in order to minimize the possibility that a G-SIB would get into difficulties. While this approach can lead to free riding, having clarity on resources at risk in general increases accountability and enhances incentives to critically evaluate home and host country supervision and increase cooperation, including through better information sharing, and thereby reduce overall risks and costs.

4.3.c. Territoriality

Under the territorial approach, which is a non-cooperative solution, there would be no presumption of sharing of assets internationally in case (parts of) a G-SIB were to become distressed. Each unit of a G-SIB would be resolved according to local laws and considering only local assets and liabilities. Since markets anticipate these actions, as soon as there are stresses, there could be runs at parts of the group, not just in the particular country facing stress. Countries would therefore require units in their jurisdictions to ring-fence all of their activities under a particular authority’s domain so as to ensure that ex post resolution can be done on a standalone basis. Concretely, this ring-fencing often means full subsidiarization of all foreign affiliates. 14

The advantages of this approach are no need for international burden sharing, as all units are fully resolved domestically, and better incentives for local supervision, as responsibilities are clearly assigned, with less scope for conflicts. The disadvantages are that global banks, since they will have to use the subsidiary model in every jurisdiction, will have the additional costs of tying up and not being able to easily use and allocate capital and liquidity globally, which can be especially costly in times of stress. 15 A more general worry with this model is that local authorities will have no or little concern for global interests and (even) less incentives for cooperation on supervision, even though some spillovers will likely remain. It can even lead to perverse actions in times of financial turmoil. Regulators may, for example, want to ensure sufficient assets in their jurisdiction to cover domestic liabilities in the event of failure (e.g., asset pledge requirements) and call for more ring-fencing of assets in case of stress, which may drive early intervention in other jurisdictions and lead, ironically, to a “regulators’ run on the bank.” Also, under the model, supervisory agencies will become closer to their domestic banks and related narrower national interests, creating not just regulatory risks, but also broader political economy concerns: will this model undermine the support for (financial) globalization?

4.3.d. Trend and choices

Before the GFC, there were some moves toward universality (e.g., EU directives and the United Nations Commission on International Trade Law model law for insolvency of non-financial corporations). Clearly, since then, national and territorial approaches are more in vogue. 16 As noted, there have been cases of ring-fencing and supervisory agencies using moral suasion to make sure lender banks continue to support their local operations. And some new regulations (e.g., the 2014 US foreign banking organization rules and the Volcker, Vickers, and Liikanen rules) could, even if not so intended, encourage more local approaches (for further details, see IMF, 2015 ). As such, the universal approach is (even) less likely going forward. It also makes many demands—notably, fully integrated regulation and supervision and centralized resolution with full burden sharing—and may even be unwise if it leads to free riding or is adopted inconsistently. Yet some universal elements could still be adopted. For example, procedures to be followed in case of resolution and restructuring could be harmonized. But these improvements would not suffice in times of crises.

For some countries, however, the universal approach, or a version thereof, is realistic. This is especially so for closely integrated countries, such as the EU or, more narrowly, its euro-area members. Triggered in part by various crises, EU countries have adopted the BU, which is a universal model within the euro area, as it integrates regulation and supervision (through the Single Supervisory Mechanism, located at the ECB), resolution (through the Bank Recovery and Resolution Directive, with a Single Resolution Board), and deposit insurance mechanisms (for additional information, see Goyal et al. , 2013 ). 17 While not all three elements are fully in place—notably, rules for burden sharing, including fiscal backstops, and deposit insurance are still being determined and implemented—it represents a big step forward.

4.3.e. Intermediate approach, modified universalism

For those other countries that reject the territoriality approach but, for various economic, financial, or political economy reasons, cannot sign on to universalism, there can be intermediate approaches. One form would be a new international agreement, a “Concordat.” It would build on the home–host principle that underlies the Basel supervisory framework, but it would focus on crisis management. Importantly, the Concordat would have explicit incentives built in for collaboration. It would stipulate that for banks to have access to foreign markets, there needs to be in place, besides effective supervision at home (as in the current home–host supervisory accord), credible resolution processes and clarity on cost sharing in any resolution, including forms of (public) burden sharing. 18 If the home country cannot meet these criteria, the host country would be allowed to take actions, including limiting entry or imposing restrictions on the activities of existing foreign banks.

The benefits of this approach are three-fold. First, it acknowledges that effective cooperation requires all three elements—regulation, supervision, and resolution—but in ways less demanding than the universal approach. It would also build on the many already existing forums to enhance cooperation—including, besides the supervisory accord, the supervisory colleges, and crisis management and financial stability groups—but acknowledge that these do not suffice, since their focus is still largely on supervision. Second, it would stress more the need to harmonize rules for resolution, neglected in the past, which would help reduce the scope for conflicts, thereby enabling better cross-border resolutions. Third, and most important, it would create incentives for de facto supervisory cooperation by more explicitly using sticks and carrots. By including resolution and requiring minimal cooperation, as countries can limit entry, it would move from “can authorities cooperate” (using MoUs) to “will authorities cooperate” (using incentives and agreements).

4.3.f. Effect of changes in global banking structures

The changes underway in the global banking system have (additional) policy implications. The rise of emerging markets’ and developing countries’ banks abroad means they need to adequately regulate their foreign affiliates and local subsidiaries. It also means they should (be allowed to) become more active in international deliberations about and decisions on financial reforms, also to give more legitimacy to these bodies and rules. And, as they become more important creditor and home countries, it will be important for them to (better) monitor cross-border lending and local lending by their foreign active banks. This monitoring will require better data to gauge developments in global banking, including whether there is indeed a general retrenchment and fragmentation in cross-border lending or whether new players are filling the gap left by retreating banks. 19

The increase in regionalization is another important development with benefits, but also risks. With more regionalization, coordination in supervising and dealing with the failures of internationally active banks could be easier, with the European BU the prime example of the potential, but many other regions have yet to formalize deep cooperation in all elements. More regionalization could also increase financial stability by leading to more local funding and greater commitments. Increased regional coordination, however, could also lead to (as well as be caused by) policies and actions that amount to financial repression, ring-fencing, and fragmentation, with adverse consequences for risk-sharing and financial stability. As such, regionalization will have to be accompanied by assurances of countries to maintain open borders.

So far, few countries have reneged on their commitments to liberalize their markets to others. But more is needed, as many, often subtle, barriers still hinder the operations of financial firms across borders, in spite of numerous initiatives. Conversely, without further detailed agreements, it may be difficult to ensure that any beneficial influences of the newly adopted macroprudential tools, such as the countercyclical capital buffer, are not being negated by foreign banks and other financial institutions in jurisdictions not subject to such rules. Coordination more generally can be needed so that policies, including macroprudential and capital flow management, create neither adverse spillovers to other financial markets nor undue barriers to cross-border banking. 20

4.3.g. Summing up

A globally universal approach to resolution is not likely soon or necessarily wise. For some closely integrated groups of countries, however, a near-universal approach is possible and, indeed, being phased in for the euro zone in the form of the BU. Some other countries could choose to adopt an intermediate approach, possibly in the form of a new Concordat, which offers sticks and carrots to make it effective. For most others, it will be important to avoid a race to the bottom, with the biggest risk being wide-scale adoptions of the territorial approach. That would be an overall adverse outcome, given its ring-fencing, more home bias, fragmentation, and perverse political economy dynamics. And, with respect to ongoing reforms, the changes in global banking make it important to take greater account of emerging markets’ and developing countries’ positions.

There are both benefits and risks associated with global banking, with the exact tradeoffs varying by many factors. While in some areas conclusions can be drawn, there are many remaining questions. I organize the state of knowledge and questions under three headings: efficiency, financial stability, and regulation. I discuss these in general and with specific reference to the ongoing changes in global banking that highlight some old and some new issues.

In terms of efficiency, including access to finance and economic growth, the literature has made clear that to analyze and then to weigh the benefits and risks of global banking, it is important to consider explicitly heterogeneity. Country conditions, institutional development, and economic circumstances—as well as the source, destination, type, and form of capital flows and foreign bank presence—crucially affect global banking’s effect on domestic financial systems and economies. Research, especially from before the GFC, suggests that “better” countries tend to get both more growth and risk-sharing benefits from global banking. So it is important for countries to ensure that they have the right regulations and infrastructure (e.g., supervision, information, and property rights) in place. While the risk-sharing aspect of this view has been questioned post-GFC, as advanced countries saw greater volatility in capital flows, many elements likely remain valid for assessing the economic growth benefits. Research also suggests that it is important to consider the origins, types, and forms of capital flows and foreign banks present. Some flows, like FDI, are more likely beneficial than short-term debt flows. Larger banks and those that are closer, with a greater share of domestic financial intermediation, including in deposit-taking, tend to provide better access to finance for SMEs and are less likely to engage in cherry-picking. And the spillovers to the domestic system are also larger, the greater foreign banks’ local footprint.

In terms of financial stability, one of the main lessons, especially since the GFC, is that global banking has two sides: risks can be exported at times but be imported at other times. As such, the net risk-sharing benefits of global banking have to be analyzed over a full cycle. And, similarly to the lessons on the effects on efficiency, it is important to consider heterogeneity since the forms and types by which global banking occurs matter for stability. Some flows, such as short-term bank debt, are more volatile, and some types of investors, such as mutual funds and international banks, are more affected by global financial and monetary conditions, exposing countries more to volatility. While countries can mitigate them, including by stricter regulations, they cannot fully eliminate the effect of global conditions unless they also give up on capital account openness. In general, foreign banks tend to support local operations when those are faced by a domestic or external shock. Research does suggest, however, that banks with greater “commitment,” as reflected, among other factors, in being relatively close to HQs, having larger local market shares and more local funding strategies, and being less engaged in transaction types of intermediation activities, are more willing to incur the temporary costs when faced with (external) shocks, maintain their operations, and support the local economy. At the same time, while there can thus be preferences for the forms in which capital flows and foreign bank presence occur, how to encourage them without creating distortions is challenging.

In regulation and supervision, in light of experiences and lessons, there has been progress in adapting paradigms so as to maximize the benefits and limit the risks of global banking. Countries now more closely monitor capital flows, not just for their type—e.g., debt versus equity—and destination—banking system versus corporate sector—but also as to which are the final investors—e.g., banks versus institutional investors or mutual funds. Relatedly, many supervisory agencies no longer rely only on the home supervisor of the local foreign affiliates. In terms of foreign banks’ regulations, there has been a trend toward a more standalone model, through subsidiarization and capital and liquidity requirements on branches. Even with better monitoring and microprudentially motivated supervisory actions, though, risks will remain with global banking, including higher chances of financial booms and busts. For many countries, macroprudential policies can help reduce these risks. For some countries, well-designed capital flow management tools are, in addition, possibilities to reduce remaining risks. For others, options can be more circumscribed—for example, as they are part of a currency union or have free trade and other agreements that limit the use of capital flow management policies.

At the global level, the need for more reforms is also recognized. Important enhancements to the international financial architecture are needed—notably, regarding the global safety net and liquidity support mechanisms for countries running into balance-of-payments difficulties. There is also the need to enhance the framework for resolving large cross-border banks. The policy issues and choices regarding global banks, especially G-SIBs, are complex, however. While there are various, not completely exclusive, possible approaches, internal consistency is key. Regardless, a combination of national and international policy responses will be needed to ensure that global banking develops in the most beneficial way.

Besides the many issues policymakers have to confront, recent developments raise many new research issues. One is the growing importance of emerging markets’ and developing countries’ banks. Entry of these banks could increase local competition and access to financial services. Importantly, as these banks invest in countries within their region and of similar (institutional) development, they may become better at collecting and processing soft information and, as such, lend more and better to informationally more opaque borrowers in these countries, especially SMEs and households. At the same time, entry of these banks, as well as more regionalized banking systems—not only in Europe, but also elsewhere—may not allow for the globally best banking technology and know-how to be employed in every market and for capital to be allocated most efficiently. Related are questions on financial stability. When do the newly entering foreign banks add to financial stability, and when do they introduce risks? Specially, are these new owners better or worse capitalized than the traditional, advanced countries’ owners? Do they have less or more access to intrabank and other funding markets to smooth local and global shocks? How do their characteristics—like their home country, degree of funding, and business focus—matter for financial stability? Are the relevant home–host regulatory and supervisory frameworks up to par? Many of these effects are not clear a priori, and specific research, especially in those countries with profound changes, has to await more data.

More generally, there are many issues to be investigated related to the ongoing shifts and the (evolution of the) structure of global banking. An incomplete list includes the following. Are the newly emerging global network structures more or less resilient to shocks? Do the shifts in global banking networks and market structures lead to new risks? Does it matter what types of foreign banks and how much variation in foreign banks from within and outside the region there are for a particular country? If regionalization means less risk-sharing and makes the global system more prone to shocks, can a larger variety of parent banks improve diversification at the host country level and make the global banking system less prone to shocks? Is there a “preferred” mix of local banks, foreign banks from “close” countries, and global banks? The starting point for many of these questions will have to be a better understanding of the drivers of the changes in global banking, including regionalization (and possibly related fragmentation), since only then can the advantages and disadvantages of the ongoing changes be properly assessed.

A bank collects and processes various types of information to screen and monitor borrowers and projects for creditworthiness and riskiness and, more generally, to reduce agency issues. To allow a bank to offer customers financial services at better terms than other banks or providers can, it may need to be close to them ( Rajan, 1992 ; Petersen and Rajan, 2002) .

However, in a multi-country world with many banks seeking opportunities, entry decisions are not made in isolation, and all (potential) competitors need to be considered. Consistent with this notion, Claessens and van Horen (2014a) show that, besides distance, competitor remoteness—the weighted-average distance of all competing banks to a host country—also importantly drives location decisions, similar to how remoteness is useful in explaining the direction of trade flows.

Forbes (2014) distinguishes six factors affecting changes in capital flows, which can grouped in similar ways under the three headings of supply, demand, and regulatory changes: (1) a. higher cost for banks to go abroad; b. reduced access to wholesale funding; c. weakness in individual bank balance sheets; (2) weakness in the demand for loans; (3) a. repercussion of crisis-resolution packages; b. regulatory changes.

Econometrically, if lenders all face similar demand, borrower country fixed effects (if using an event) or country interacted with time fixed effects (if using a panel with multiple periods) suffice to control for demand, i.e., changes in economic activity and prospects in the borrower country. While often used, analyses do assume that all lenders face the same demand, but there can be bilateral (lender- and borrower-specific) demand curves. For this reason, some papers analyze changes in cross-border lending at the firm rather than country level (e.g., De Haas and can Horen, 2012 , 2013 ).

Some differences in the type of borrowers financed by cross-border versus foreign affiliate loans could explain variations in responses. For example, cross-border bank claims may be mostly financing large corporations, banks, and sovereigns, whereas local affiliates could cater more to retail borrowers for consumer and residential credit. If a shock affects these two classes of borrowers differently, changes in these two modes of lending could vary, even if they are done by the same bank or banking system.

The Lucas (1990) paradox is the observation that capital, on average, flows from poor to rich countries, rather than from rich to poor countries. Prasad, Rajan, and Subramanian (2007) , besides finding support for it, note that the Lucas paradox has intensified over time and there is no clear relationship between stock or flow measures of capital flows or financial liberalization and growth. Furthermore, Gourinchas and Jeanne (2013) find a negative long-run correlation between productivity growth and net capital inflows across non-OECD countries. They dub this the “allocation puzzle” and find that it is mostly a feature of public flows, with international reserve accumulation playing an important role. Alfaro, Kalemli-Ozcan, and Volosovych (2008) find evidence that institutional quality is the leading explanation. Others, such as Stulz (2005) , pointed out the limits of financial globalization in relation to (corporate) governance.

Contessi, De Pace, and Francis (2013) analyze the second moments and cyclical properties of disaggregated gross capital flows with respect to three macro variables: GDP, investment, and real interest rates. They find in most countries that debt is the most volatile and debt inflows are procyclical with respect to all three macro variables except for advanced countries between 1992 and 2005. See also Broner et al. (2013) .

At the same time, Cetorelli and Goldberg (2012b) show that, when faced with a funding shock, global banks tend to reallocate capital within the holding toward “important” subsidiaries. While they do not study the lending behavior of subsidiaries, their results suggest that some affiliates might be forced to curb lending because of a reduction in funding from the parent, whereas other affiliates do not feel this pressure or might even be in a better position compared with domestic banks to continue to extend credit. See Correa, Goldberg, and Rice (2015) for an analysis of the internal market operations of US global banks and related effects on lending growth, also in comparison with domestic US banks.

In IBRN-coordinated analyses, the spillovers of macroprudential policies were analyzed using microdata and were generally found to be relatively small ( Buch and Goldberg, 2016 , provide the overview; for specific country examples, see Reinhardt and Sowerbutts, 2015 ; Nocciola and Żochowski, 2015) . Besides macroprudential policies, other regulatory actions can have (unintended) side effects reducing (the benefits of) financial integration, including possibly amounting to financial protectionism. For further details, see Beck et al. (2015) for a classification of such policies and Ostry et al. (2011) on the links between macroprudential and capital flow management policies.

As part of government support, banks were often asked to focus on domestic lending. For example, French banks that tapped government assistance pledged to increase lending by 3–4 percent annually, and ING announced that it would extend €25 billion to Dutch businesses and consumers when it received another round of government assistance ( World Bank, 2009 , page 70, footnote 9). Also, support measures that ended up going to foreign banks were criticized ex post by politicians. Rose and Wieladek (2014) and Kleymenova, Rose, and Wieladek (2015) find some evidence of protectionism using UK and US bank data in that banks lend more at home and in similar ways after the GFC. As the literatures on home-bias and bank-sovereign links make clear, however, it is hard to separate the various motives for increased lending to local firms, households, and sovereigns. These include, besides protectionism, banks having greater information about borrowers closer by, internal market frictions, and banks’ preferred risk-return tradeoffs, some of which may appear as nationalism (e.g., buying risky own sovereign bonds; see Acharya and Steffen, 2015) .

TLAC comes in the form of debt that can be written down and has to satisfy some other conditions, in part to avoid contagion in case of write-downs. It requires banks to have prepositioned enough debt at the holding company and at its various entities so as to increase the chances it can be “bailed in” and continue its businesses. As the FSB (2015b) states in its press release on November 9, 2015, “The TLAC standard is designed to ensure that if a G-SIB fails it has sufficient loss-absorbing and recapitalization capacity available in resolution to implement an orderly resolution that minimizes impacts on financial stability, ensures the continuity of critical functions, and avoids exposing public funds to loss.”

Bordo and James (2014) added three: the incompatibility of fixed exchange rates and capital mobility with financial stability; the potential incompatibility of fixed exchange rates and free movement of capital with democracy; and the incompatibility of capital flows, democracy, and a stable international political order. Since, as cross-border financial integration progresses, policymakers will have less scope for independent policymaking, including fiscal independence, Rodrik (2007) questioned the combination of nations (sovereignty), democracy, and globalization. Pisani-Ferry (2012) questioned, in the context of the euro area, the combination of a monetary union, national banking systems, and a lack of common fiscal responsibility, which is close to the financial trilemma. For further details, see Obstfeld (2015) and Rey (2013) .

Maximizing global welfare can mean considering not only global financial stability, but also other global objectives, such as the reliability of financial contracting and the efficiency of global allocation of funds, which can raise other tradeoffs. These are not reviewed here.

The G-SIB’s organizational structure that fits best with this paradigm is where each subsidiary is also functionally independent, i.e., it has its own treasury and other critical functions, since this independence makes institutions easily resolvable locally not only because they are operating in each jurisdiction through separately incorporated entities, but also because they do not depend on other entities in the group for critical functions.

Restrictions on shifting funds could cause unnecessary runs and insolvencies in some parts of the group as global banks become strapped for cash (capital) even though the group as whole may be liquid (solvent). Also, under this model, some channels for international spillovers would still remain. For example, the insolvency of a subsidiary of a G-SIB operating under the same name as its parent could affect the ability of the rest of the group to attract funds, even without direct financial spillovers. Moreover, cross-border banking and other forms of capital flows would presumably still be allowed, with associated risks affecting banks.

And even earlier, there were notable exceptions, where local claims were often satisfied first, such as the case of the Bank of Credit and Commerce International. A related case is the US deposit preference rule, which makes US deposits trump other claims in bankruptcy. The USA generally follows a territorial approach with regard to US branches of foreign banks: it conducts its own insolvency proceedings based on local assets and liabilities. Assets are transferred to the home country only if and when all local claims are satisfied. As Baxter, Hansen, and Sommer (2004 , p. 61) note, in the USA, although the nationality of creditors is irrelevant, “only creditors of the local branch of the insolvent firm may participate. . . . On the asset side, the insolvency official asserts jurisdiction over all local assets and assets outside the jurisdiction that are ‘booked’ to the jurisdiction.”

Note that there was an earlier proposal called the European Bank Charter (EBC) ( Chihak and Decressin, 2007 ; see also Decressin, Faruqee, and Fonteyne, 2007 ). The EBC was meant as a new regime, where mainly cross-border (EU) banks could apply and be rechartered as an EU bank, i.e., in one jurisdiction and legal form. They would be subject to a single supervisory authority and presumably equipped with all of the necessary tools, including resolution authority and possibly some burden sharing. Although it was intended to be phased in, as it still had many institutional demands, this model did not get much traction.

The international standard is the so-called Key Attributes of Effective Resolution Regimes (KA); for further details, see IMF (2010) and FSB (2014) . The KA is a non-binding set of principles, with the objectives to resolve financial institutions in an orderly manner while minimizing the costs for taxpayers from rescues and ensuring continuity of critical economic functions. It is being used to assess countries’ resolution regimes (see IMF, 2014) and accords with the resolution approach underlying TLAC. Complementarily, it will be necessary to improve the structures of G-SIBs and enhance the ability to wind them down in an orderly way in case of weaknesses. And it would require greater convergence in many other national practices and rules besides resolution, including those covering definitions of capital (adequacy); contingent capital, such as TLAC; and associated (regulatory) triggers.

For example, the BIS IBS data include only a few emerging markets as reporting creditor countries, so they cannot capture the likely growing emerging markets’ and developing countries’ lending (for additional information, see Cerutti, Claessens, and McGuire, 2014 , on the BIS IBS data).

Even though the scope for (policy) spillovers is large, the case for international coordination and cooperation requires, of course, the presence of negative externalities, which are less obvious, and, to date, there has been limited analysis of welfare gains from coordinating macroprudential policies (see Jeanne and Korinek, 2014) .

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