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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.

figure 1

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.

figure 2

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.

figure 3

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.

figure 4

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.

figure 5

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

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Broby, D. Financial technology and the future of banking. Financ Innov 7 , 47 (2021). https://doi.org/10.1186/s40854-021-00264-y

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research papers on commercial banks

Monetary Tightening, Commercial Real Estate Distress, and US Bank Fragility

Building on the work of Jiang et al. (2023) we develop a framework to analyze the effects of credit risk on the solvency of U.S. banks in the rising interest rate environment. We focus on commercial real estate (CRE) loans that account for about quarter of assets for an average bank and about $2.7 trillion of bank assets in the aggregate. Using loan-level data we find that after recent declines in property values following higher interest rates and adoption of hybrid working patterns about 14% of all loans and 44% of office loans appear to be in a “negative equity” where their current property values are less than the outstanding loan balances. Additionally, around one-third of all loans and the majority of office loans may encounter substantial cash flow problems and refinancing challenges. A 10% (20%) default rate on CRE loans – a range close to what one saw in the Great Recession on the lower end -- would result in about $80 ($160) billion of additional bank losses. If CRE loan distress would manifest itself early in 2022 when interest rates were low, not a single bank would fail, even under our most pessimistic scenario. However, after more than $2 trillion decline in banks’ asset values following the monetary tightening of 2022, additional 231 (482) banks with aggregate assets of $1 trillion ($1.4 trillion) would have their marked to market value of assets below the face value of all their non-equity liabilities. To assess the risk of solvency bank runs induced by higher rates and credit losses, we expand the Uninsured Depositors Run Risk (UDRR) financial stability measure developed by Jiang et al. (2023) where we incorporate the impact of credit losses into the market-to-market asset calculation, along with the effects of higher interest rates. Our analysis, reflecting market conditions up to 2023:Q3, reveals that CRE distress can induce anywhere from dozens to over 300 mainly smaller regional banks joining the ranks of banks at risk of solvency runs. These findings carry significant implications for financial regulation, risk supervision, and the transmission of monetary policy.

We thank seminar and conference participants at Stanford, Northwestern, Hoover, Columbia, UCLA, Fannie Mae, Mortgage Bankers Association, NBER Corporate Research Associates Symposium, and Commercial Real Estate Data Alliance Research Symposium for helpful comments. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

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The effect of credit risk management and bank-specific factors on the financial performance of the South Asian commercial banks

Asian Journal of Accounting Research

ISSN : 2459-9700

Article publication date: 14 October 2021

Issue publication date: 27 May 2022

Among all of the world's continents, Asia is the most important continent and contributes 60% of world growth but facing the serving issue of high nonperforming loans (NPLs). Therefore, the current study aims to capture the effect of credit risk management and bank-specific factors on South Asian commercial banks' financial performance (FP). The credit risk measures used in this study were NPLs and capital adequacy ratio (CAR), while cost-efficiency ratio (CER), average lending rate (ALR) and liquidity ratio (LR) were used as bank-specific factors. On the other hand, return on equity (ROE) and return on the asset (ROA) were taken as a measure of FP.

Design/methodology/approach

Secondary data were collected from 19 commercial banks (10 commercial banks from Pakistan and 9 commercial banks from India) in the country for a period of 10 years from 2009 to 2018. The generalized method of moment (GMM) is used for the coefficient estimation to overcome the effects of some endogenous variables.

The results indicated that NPLs, CER and LR have significantly negatively related to FP (ROA and ROE), while CAR and ALR have significantly positively related to the FP of the Asian commercial banks.

Practical implications

The current study result recommends that policymakers of Asian countries should create a strong financial environment by implementing that monetary policy that stimulates interest rates in this way that automatically helps to lower down the high ratio of NPLs (tied monitoring system). Liquidity position should be well maintained so that even in a high competition environment, the commercial is able to survive in that environment.

Originality/value

The present paper contributes to the prevailing literature that this is a comparison study between developed and developing countries of Asia that is a unique comparison because the study targets only one region and then on the basis of income, the results of this study are compared. Moreover, the contribution of the study is to include some accounting-based measures and market-based measures of the FP of commercial banks at a time.

  • South Asian countries

Credit risk

Bank-specific factors.

  • Generalized method of moment

Siddique, A. , Khan, M.A. and Khan, Z. (2022), "The effect of credit risk management and bank-specific factors on the financial performance of the South Asian commercial banks", Asian Journal of Accounting Research , Vol. 7 No. 2, pp. 182-194. https://doi.org/10.1108/AJAR-08-2020-0071

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Copyright © 2021, Asima Siddique, Muhammad Asif Khan and Zeeshan Khan

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Introduction

Around the globe, depository institutions perform a crucial job in bringing financial stability and economic growth by mobilizing monetary resources across multiple regions ( Accornero et al. , 2018 ). The commercial plays an intermediary role by collecting the excessive amount from savers and issuing loans to the borrowers. In return, banks can earn a high interest rate ( Khan et al. , 2020 ; Ghosh, 2015 ). Banks tried to increase their financial performance (FP) by issuing loans while playing their intermediary role; banks have a high chance of facing credit risk. Accornero et al. (2018) found that the country's banking industry mostly collapses due to high credit risk. Sometimes, it leads to the failures of the whole financial system. Credit risk is expected to be arises when a borrower cannot meet their obligation about future cash flows. Commercial banks' FP is affected by two factors: one is external and the other is internal. Bank-specific factors are internal and able to control factors of the commercial banks. Ofori-Abebrese et al. (2016) pointed out that adverse selection and moral hazards were created due to mismanagement of internal factors. The abovementioned financial problems are turmoil period in the banking/financial sector.

Among the entire continent of the world, Asia is the most crucial continent and contributing 60% of world growth but facing the serving issue of high nonperforming loans (NPLs). It is well known that a high ratio of NPLs weakens the economy or country's financial position. The growth level in South Asia was the highest in 2015, and the ratio is 9.3%, which is the highest among all continents. According to the Asian Development Bank (2019), the NPLs in the south are approximately $518bn, which is relatively high compared to previous years. The soaring of NPLs in South Asian countries enforces a massive burden on commercial banks' financial position (mainly banks' lending process effected). The massive increase in NPL is observed after the global financial crisis (2007–2008). According to Masood and Ashraf (2012) , the credit risk high ratio of NPLs is the main reason for most of the financial crisis because NPLs alarmingly high during the Asian currency crisis in 1997 and subprime crises in 2007, and some loans are declared bad debts. The alarmingly high ratio of NPL resulted in an increasing depression in the financial market, unemployment and a slowdown of the intermediary process of banks (see Figure 1 ).

The World Bank statistics of different regions show that NPLs exist in almost all regions. Still, the ratio of NPLs is relatively high in the South Asian area compared to other regions. Therefore, the study is conducted in South Asia. Two proxies of credit risk are used in this study: NPLs and capital adequacy ratio (CAR). Moreover, the study also incorporates bank-specific factors to increase FP.

Various studies ( Louzis et al. , 2012 ; Ofori-Abebrese et al. , 2016 ; Hassan et al. , 2019 ) are conducted to address the issue, but literature shows that the results of these studies are inconclusive and also ignore the most important region of South Asia. Therefore, the study objective is to investigate that credit risk and banks specific factors affect FP of commercial banks in Asia or not? We have selected two from South Asia, Pakistan and India, as sample countries. In 2019, the NPLs were 13% and 10% in Pakistan and India, respectively. This ratio is relatively high as compared to the other countries of the world. Due to these reasons, we have mainly selected India and Pakistan from South Asian countries ( Siddique et al. , 2020 ). The present study uses secondary panel data set of 19 commercial banks from 2009 to 2018.

Two serious threats may exist: The first is autocorrelation and the second is endogeneity. If the data do not meet these CLRM assumptions, then the regression results are not best linear unbiased prediction (BLUE) ( Sekaran, 2006 ; Kusietal, 2017 ). And in this situation, apply pooled regression is applied, and then the results were biased because the coefficient results cannot give accurate meaning. After all, pool regression ignores year and cross section-wise variation. Therefore, in this study, an instrumental regression can be used that handle all these issues. Generalized method of moments (GMM) is used to analyze the data to overcome endogeneity. Our study is unique by addressing the autocorrelation and endogeneity issue at a time. Our study results show that credit risk measure NPLs decrease the FP due to having negative relation, while CAR has a positive relation with South Asian banks’ FP. The remainder of the research study is organized as follows: Section 2 consists of a detailed literature review; Section 3 consists of data and methodology. Sections 4 contains information about finding and suggestions. Finally, Section 5 discusses the conclusion.

Literature review

The Literature Review has mainly divided into two crucial sections; First part consists of the literature review related to credit risk and FP. The other part is related to the literature review of bank-specific variables and FP. In the hypothesis development, we have used commercial banks' profitability that represents the FP of commercial banks.

Credit risk and financial performance

While operating in the banking industry, three categories of risks that the bank has to face include environmental, financial and operational risks. Banks generate their incomes by issuing a massive amount of credit to borrowers. Still, this activity involves a significant amount of credit risk. When borrowers of the banking sector default cannot meet their debt obligation on time, it is called credit risk ( Accornero et al. , 2018 ). When there is a large amount of loan defaulter, then it adversely affects the profitability of the banking sector. Berger and DeYoung (1997) pointed out that the absence of effective credit risk management would lead to the incidence of banking turmoil and even the financial crisis. Siddique et al. (2020) explain that NPLs are related to information asymmetric theory, principal agency theory and credit default theory. When asymmetric information unequal distribution of information of high NPLs is spread, there is a chance that banks or financial declared bankrupt. According to Pickson and Opare (2016), the principal agency must separate corporate ownership from managerial interest. Because each management has its interest, they want more prestige, pay increment and want the stock options for management. Effective management of credit risk or nonperformance exposure in the banking sectors increases profitability. It enhances the development of banking sectors by adequate allotment of working capital in the economy ( Ghosh, 2015 ).

There is a growing literature ( Louzis et al. , 2012 ) on credit risk and its empirical relationship with the monetary benefits of the banking sector. Ekinci and Poyra (2019) investigate the relationship between credit risk and profitability of deposit banks in Turkey. The data sample used 26 commercial banks from 2005 to 2017. All data of this study are secondary and collected from annual reports of commercial Turkey banks. The proxies of profitability were taken as return on equity (ROE) and return on the asset (ROA), while NPLs of commercial banks were used as a proxy to measure credit risk. The research paper reveals that credit risk and ROA are negatively correlated as well as the relation between credit risk and ROE is also significantly negative relation. Therefore, the study suggests that the Turkey government tightly monitors and controls the alarmingly soaring ratio of NPLs. Upper management introduced some new measures to trim the credit risk.

There is a negative and significant relationship between NPLs and commercial banks' FP.

There is a positive and significant relationship between capital adequacy ratio and commercial banks' FP.

Bank-specific variables and financial performance

Bank-specific variables or internal factors are the product of business activity. Diversifiable risk is associated with these factors ( Louzis et al. , 2012 ) and can be reduced by efficient management. This risk is controllable compared to an external factor, which cannot be diversified because this risk is market risk ( Ghosh, 2015 ; Rachman et al. , 2018 ). If a firm can manage its internal factor effectively, then the firm can be high profitability, while, on the other hand, these factors are mismanaged. It would adversely affect the firm's balance sheet and income statement ( Ofori-Abebrese et al. , 2016 ). Different authors ( Akhtar et al. , 2011 ; Louzis et al. , 2012 ; Chimkono et al. , 2016 ; Hamza, 2017 ) discuss different bank-specific variables and firm performance in their studies. The bank-specific variables used in this study are cost-efficiency ratio (CER), average lending rate (ALR) and liquidity ratio (LR). Aspal et al. (2019) used two types of factors (macro and bank-specific factors) and inspected their connection with the FP of the commercial bank in India. Gross domestic product (GDP) and inflation are used as proxies of macroeconomic factors.

In contrast, a bank-specific variables’ proxy includes capital adequacy ratio, asset quality, management efficiency, liquidity and earnings quality. Data of 20 private banks have been used from 2008 to 2014. The panel data pointed out that one macroeconomic factor is significant (GDP), and another factor (Inflation) is insignificant. All bank's specific factors (earning quality, asset quality, management efficiency and liquidity) significantly affect the FP except the CAR (insignificant). Hasanov et al. (2018) conducted their study to explore the nature of the interrelation between bank-specific (BS) and macroeconomic determinants with the banking performance of Azerbaijan (oil-dependent economy). The study used the GMM to analyze the panel data set. The results show that bank loans, size, capital and some macro factors (inflation, oil prices) were positive and significantly interconnection with the FP of banks; on the other hand, liquidity risk, deposits and exchange rates are significantly affected negatively bonded with the FP.

There is a negative and significant relationship between the CER and commercial banks' FP.

There is a positive and significant relationship between the ALR and commercial banks' FP.

Francis et al. (2015) define liquidity in their study and, according to the liquidity of an asset, determined by how quickly this asset can be converted or transferred into cash. Liquidity is used to fulfill the short-term liabilities rather than the long term ( Siddique et al. , 2020 ; Raphael, 2013 ). Adebayo et al. (2011) mentioned in their study that when banks are unable to pay the required amount to their customers, it is considered bank failure. Sometimes liquidity risk affects the whole financial system of a country. Different studies are conducted on the issue of liquidity and performance, but different studies show different results. FP and liquidity, on the other hand, a chunk of studies ( Francis et al. , 2015 ; Hamza, 2017 ) revealed significant negative tie-up between liquidity and FP, while some other studies pointed out that there is no significant relationship between liquidity and FP. Therefore, the studies show a contradictory result, so the current study takes the bank-specific measures (LR, ALR study and CER) and checks its interconnection with commercial banks' FP.

There is a positive and significant relationship between the LR and commercial banks' FP.

Data and methodology

Our current study has one problem variable, financial performance (FP), while regressors variables are credit risk and bank-specific variables. Our model is consistent with Chimkono et al. (2016) , where ROA and ROE will be used as a measure of FP, while credit risk will be measured by NPL ratio, CAR and three specific variables: CER, LR and ALR.

Various studies ( Hamza, 2017 ; Belas, 2018 ) emphasize some macro and micro variables that need to be controlled when measuring FP because these factors are the influential factors. Three control variables: size of the bank, age of the banks and Inflation are used in this study and shown as yes in the tables. We have chosen these three control and most relevant variables because these variables represent both micro and economic situations. Data have been collected from two South Asian countries Pakistan and India. The nature of data is panel data and the number of banks from Pakistan (10 commercial banks) and India (9 commercial banks) is 19. The data have been collected from bank financial statements throughout 2009 to 2018, so the data of this study are a panel in nature. The final number of observations is 190 (19*10 = 190) for the analysis of this study (see Table 1 ).

Operational definition

The probability of lenders being the default, high credit risk higher FP of banks ( Louzis et al. , 2012 ).

Bank-specific factors are those which are under the control of the management of commercial banks ( Chimkono et al. , 2016 ).

Nonperforming loans

A loan becomes nonperforming when the duration of the loan has been passed, and after that duration, banks 90 days are passed unable to receive the principal amount of loan and interest payment ( Hamza, 2017 ).

Methodology

The current study investigates the interrelationship between credit risk, bank-specific factors and FP. Panel data set is used in our study, and two serious threats usually faced when using panel data set: (1) autocorrelation and (2) endogeneity. For this purpose, a GMM can be used. GMM model has many advantages on simple ordinary least square regression. And when in any study GMM model applies, it allows by adding the fixed effect model; this model can be able to tackle the problem of heterogeneity, and it also removes the problem of endogeneity by introducing some instrumental variables.

Model specification

The regression model is as follows:.

γ 0  = intercept; γ 1 - γ 8  = estimated coefficient of independent variables and control variables.

ε it represents error terms for those variables that are omitted or added intentionally/unintentionally.

According to Lassoued (2018) , panel data regression has two significant problems: autocorrelation and endogeneity, and this problem is existed due to the fixed effect. Therefore, our study checked the basic two assumptions of ordinary least squares.

Testing for autocorrelation

The fifth assumption of CLRM is that data should be free from autocorrelation. Sekaran (2006) pointed out the relationship between two different error terms should be zero; it means that there is no autocorrelation between error terms. There are different tests for testing autocorrelation, but the Wooldridge test is used in the present paper to test the autocorrelation.

Table 2 shows that the p -value of the Wooldridge test result is zero, so it means that all p -values are less than 0.05. It means that reject the null hypothesis. And the null hypothesis is that our data have no autocorrelation, but the results show that our data have autocorrelation problems.

Testing for endogeneity

The seventh assumption of CLRM is that data have no issue of endogeneity. Sekaran (2006) found that the relationship between the error term and explanatory or independent variable should be zero. If this relationship is not zero, then the problem of endogeneity exists. Brooks (2014) pointed out that Hausman test results probabilities can be used to test the endogeneity, and the null hypothesis of this test is that errors are uncorrelated. He also pointed out that if the probabilities are more than 0.10, then accept the null hypothesis. It means that there is no problem of endogeneity, and if the values are less than 0.1, then our data have the problem of endogeneity. Appendix 1 shows that some values of the Hausman test are less than 0.10, so it means that data have the problem of endogeneity. Our panel data results prove that our data have the problem of autocorrelation and endogeneity. Some CLRM model assumptions are not met, so ordinary least square regression results are not BLUE. And GMM model can be applied to any study because this model can be able to tackle the problem of autocorrelation, and it also removes the problem of endogeneity by introducing some instrumental variables.

Findings and discussion

The present research paper provides empirical evidence on the interconnection between credit risk and bank-specific/internal factors on FP commercial banks. To analyze the data set, first, the study applies the descriptive analysis to identify the big picture of the data, then the correlation section and at the end, regression results are discussed. Table 3 presents the descriptive statistics of the all variables used in the study: credit risk indicator which are the ratio of NPL, CAR; indicators of bank-specific factors (CER, ALR, LR); some control variables SIZE, AGE, INF and the measure of FP: ROA, ROE. The mean value of ROA and ROE is 0.986 and 7.964 with a standard deviation of 1.905 and 39.175, respectively, which shows that ROE has much higher variation than ROA. The standard deviation of NPL is 9.659, which is double that of CAR, whose standard deviation is 4.183 among all bank-specific factors (see Table 4 ).

Factor (CER, ALR, LR) LR has high dispersion (14.177) because there is a remarkable difference between minimum 25.027 and maximum value (107.179) of LR. ROA has 0.986 with a range between 10.408 and −6.234 with a standard deviation of 1.905, and it shows that there is a low level of dispersion in developed countries. The dispersion of ROE 39.175 is highest among all other variables, which means that some outliers exist in the ROE variable.

Correlation analysis is used to check the linear relationship between the two explanatory variables ( Brooks, 2014 ). If the sample size of any approaches to 100, greater than 100 and the correlation coefficient is 0.20, then the correlation is significant at 5% ( Lassoued, 2018 ). Most of the variables in the current study are significant at 5%.NPLs, and CER loans are negatively correlated with almost all independent variables, which supports the literature point that NPLs and CER are negatively associated with FP and bank-specific factors. The negative correlation of NPLs with ROE is loan −0.378, and this correlation is high as compared to other countries. At the same time, all bank-specific factors, CER, ALR and LR are mostly positively correlated with most of the other, almost all dependent and independent variables, while AGE and INF are mostly negatively correlated with the other variables of the study.

Regression results and discussion

Tables 5 and 6 have shown the regression results of pooled regression and GMM models. Tables include all independent, control variable coefficients, t -statistics, standard error and probability values. Additionally, tables have the values of R 2 , adjusted R 2 and Durbin Watson statistics. The adjusted R 2 under pooled regression are 0.250 and 0.231 in both models (ROA and ROE). While adjusted R 2 under the GMM are 0.358 and 0.249 in both models ROA and ROE.

It means the GMM more and better explains our model than pooled regression. Moreover, we also apply a Hausman test on both models. The p -value of both models is less than 0.05, so our data have the problem of endogeneity null hypothesis. To eliminate the endogeneity issue, the GMM coefficient was measured.

NPL has a significant and negative measure of FP: ROA and ROE. In contrast, CAR has significant and positive with all proxies of FP: ROE and ROA, which supports H1 and H2 of the paper. Our finding is consistent with Masood and Ashraf (2012) who conducted their study on credit risk and FP and found a significant negative relationship between NPL and FP, so NPLs hinder banks' profitability. Therefore, NPLs affect the whole financial system of a country especially in developing countries. The findings of CAR matched with Accornero et al. ’s (2018) study and pointed out that CAR has a significantly positive link with FP. CER has a significant negative relationship with ROA and ROE, which is consistent with the study of Francis et al. (2015) who pointed out a significant negative relationship between CER and ROE. Therefore, banks need to adapt strategies to control these costs and tried to increase their profitability. ALR had a significant and positive relationship with both measures of FP. ALR is significant at 1% with ROA and 10% significant with ROE. The result is supported by the study of Chimkono et al. (2016) who found a positive relationship between the ALR and FP of commercial banks.

LR has a significantly negative relationship with ROA and ROE. This finding is consistent with Siddique et al. (2020) who pointed out a significant negative relationship between LR and ROE; the more liquidity is maintained, the lesser the profitability level. In short, most of the independent variables are significant at 5% and 1%, and control variables are also significant in both models size of the bank and inflation except AGE. This result is matched with Ghenimi et al. ’s (2017) findings that prove that total assets or investment increment are directly proportional to the FP. Both variables of credit risk NPL and CAR are significant with the FP of commercial banks in both models. Banks try to reduce bank-specific factors risk, and by doing so, ultimately the amount of bad debt decreased, and another benefit is that it also reduces the amount of loan loss provision.

The current study empirically investigates the causal interrelation between credit risk, bank-specific factors and FP of commercial banks in two South Asian countries (Pakistan and India). The study's finding suggests that managers in South Asian countries should be focused on increasing capital adequacy to enhance the monetary gain (FP) while for the contraction of NPLs by implementing modern techniques and strategies for credit risk (NPLs) management. One indicator of the bank-specific variable (ALR) has a significant and positive interrelation with the FP of commercial banks. In contrast, CER and LR have a significant and positive relationship with the FP of commercial banks of South Asia. Control variables of the study (size of the bank and inflation) are also significant in both models except AGE. There are several policy implications that commercial banks of South Asian countries should be followed. NPLs are soaring due to the following reasons: less supervision and monitoring of customers, the problem of the market and lack of customer knowledge related to loans. Bank management should be efficient in judging that their customers have viable means of repayment or not. Moreover, banks can offer expert opinion to the professional loan take on feasible techniques of efficiently endow the borrowing to secure the required return on total firms investment is acquired. Liquidity position should be well maintained so that even in a high competition environment, the commercial can survive in that environment.

The scope of the study is only limited to commercial banks, but this model can also be applied to Islamic banks. And future researchers can also apply this model to a comparison-based study of commercial and Islamic banks. Data of this study have been collected only from 19 banks; future research can also increase the number of banks and increase the number of years to conduct their study. And if the number of banks and the number of the year increased, the results are a more reliable and accurate representation of the population. The data of this study have been taken only from two countries of South Asia, but this study can be extended by adding more countries in Asia. When we add the number of countries, the results are a better and accurate representation of developing and developed countries of Asia. This model can also be applied to some other continents because the macro environment and bank-specific factors are pretty different from continent to continent Appendix A1 .

research papers on commercial banks

NPLs-continent wise

Summary of explanatory variables and dependent variables

Results for autocorrelation for South Asia countries

Descriptive statistics

Correlation figures

ROA model (pooled regression and fixed effect GMM result)

Extra tables and figures in the Google drop box and available at: https://www.dropbox.com/sh/dro0gkowf3t542r/AAC3QQ5lKQTpLdke7UNxRUEea?dl=0

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NPAs and profitability in Indian banks: an empirical analysis

  • Santosh Kumar Das   ORCID: orcid.org/0000-0002-2685-3971 1 &
  • Khushboo Uppal 1  

Future Business Journal volume  7 , Article number:  53 ( 2021 ) Cite this article

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As financial intermediaries, the commercial banks to a large extent depend on the performance of their lending as a critical source of earning. Due to increasing loan failures, the share of non-performing advances has increased substantially in recent years, thereby adversely impacting their profitability. The paper has examined the NPAs and profitability relationship by estimating the determinants of profitability of 39 public sector and private banks for the time period from 2005 to 2019. Using a set of bank specific and macroeconomic predictors of profitability, we found that NPA has negative impact on the rate of profit of the Indian banks. The study suggests that the banks must reduce their NPAs and operating cost to improve their profitability.

Introduction

Growing incidence of non performing advances or loans can have potential adverse impact on the performance of the banks by squeezing their earnings, thereby reducing their profitability. Typically, a loan or advance becomes non-performing assets (NPAs) when a borrower defaults on the repayment of either the principal amount or unable to serve its debt. An NPA not only makes an asset unproductive, banks also fail to recover the principal capital. On the one hand, the interest earning of the bank declines; on the other side, there is a risk of recovery of principal amount. Falling interest income while directly impacts the profitability of a bank, under recovery of principal capital can result in erosion of bank’s capital base. Beyond a threshold level, the combination of both can potentially affect the stability a bank.

The Reserve Bank of India (RBI) has defined the NPAs as those assets for which principal or interest payment remains overdue for a period of ninety days. The RBI has classified three types of assets within the category of NPAs—substandard assets, doubtful assets, and loss assets [ 24 ]. A substandard asset is one if it remains as an NPA for a period less than or equal to 12 months. Similarly, a doubtful asset is defined as an asset which has remained as an NPA for a period of more than 12 months. In case of loss asset, the loss has already been identified and the amount is not written off. The combination of the above three types of assets forms total NPAs in a bank. The NPAs reduce the profitability of banks due to increase in operating costs and decline in their interest margins [ 7 , 19 ]. Studies have shown that a bank with high level of NPAs generally incurs ‘carrying costs’ on non-performing assets that reduces their profitability [ 4 ]. Also, a rise in NPA is likely to cause adverse impact on the profitability of the banks due to huge amount of provisioning requirements out of operating profits, which acts as a drain on profitability of banks. Thus, provisioning and carrying costs of NPAs act as drain on the profitability of the banks. Berger and Young [ 7 ] examined the relationship between bad loans and bank efficiency. They found that increasing incidence of loan failures forces banks to incur extra operating costs in the form of increased spending on monitoring of such assets and selling off of these loans. The banks are preoccupied with recovery procedures instead of concentrating on expanding their business. Higher the bank operating costs, lower will be the cost efficiency of banks and thus lower will be the profits. Operating costs include wages and salaries of employees and costs of running branch offices. These costs have an adverse impact on profitability of banks [ 30 ].

There are several factors, including non-performance of loans that can potentially affect the profitability of the banks. It can broadly be categorised into the bank specific, and macroeconomic factors. The bank-specific factors include non-performing advances [ 7 , 19 ], deposits [ 20 , 25 ], non-interest income [ 30 ] (Harbi 2019), interest income [ 5 ], operational efficiency [ 1 , 17 ], and capital adequacy [ 6 , 11 ]. The macroeconomic factor includes GDP growth [ 11 , 30 ], rate of inflation [ 9 ], and interest rate [ 8 , 11 , 29 ].

The present paper empirically analyses the impact of NPAs on the profitability of Indian public sector and leading private banks. Accordingly, the determinants of profitability have been estimated. The paper spreads over five sections. The introduction section has provided the background of the paper. The methodology section elaborates on the empirical strategy, data, variables and estimation model. The findings of the empirical exercise have been presented in the results section. In the discussion section, the findings of the study have been discussed. The concluding remarks have been presented the conclusion section.

Literature review

Previous studies, those have examined the relationship between the non-performance of loans and profitability of banks, have overwhelmingly concluded that NPAs have adverse impact on the profitability of the banks. There are several other factors, including NPAs that affect profitability which have been discussed in the literature.

In a study of banking sector of the US, for the period between 1970 and 1976, Martin [ 18 ] concluded that a rise in NPAs hurt the earnings of the banks, which reduces the profitability of banks. Masood and Ashraf [ 19 ] studied 25 Islamic banks from 12 countries from the Middle East, East Asian, African and South Asian regions for the period from 2006 to 2010. They found that non-performing loans negatively affects the bank performance and profitability. Ongore and Kusa [ 21 ] studied commercial banks in Kenya for the period from 2001 to 2010 and found a negative relationship between bank profitability and non-performing loans. Al-Jafari and Alchami [ 2 ] in their study of 17 Syrian banks, from 2004 to 2011, found a negative relationship between credit risk, as represented by loan loss provision, and bank profitability.

Cucinelli [ 10 ] using a sample of 488 listed and unlisted Italian banks over a period from 2007 to 2013 found that an increase in credit risk by either a rise in the non-performing loans ratio or a fall in credit portfolio quality as represented by a rise in loan loss provision ratio leads to banks to decrease their lending activity, which in turn can negatively impact their profitability. Higher NPAs results in lower bank profitability as higher NPAs require increased provisioning which eats into the profits of banks. Duraj and Moci [ 12 ] in their study of studied 16 Albanian banks between 1999 and 2014 found this negative relationship.

A study by Islam and Nishiyama [ 15 ], using data for 259 commercial banks in South Asian countries including India, for the period from 1997 to 2012, found that there is a negative relationship between non-performing loans and bank profitability. Similarly, Hashem [ 14 ] in his study of Egyptian banks for the period from 2004 to 2014 reported that higher loan loss provisions represent higher credit risk and hence lowers asset quality of banks which badly affects bank profitability. Bace [ 3 ] used data for 13,000 deposit taking institutions around the world for the period from 2014 to 2015 and found negative relationship between the NPAs and bank profitability. Similarly, a study by Etale et al. [ 13 ] that investigated the relationship between the non-performing loans and bank profitability for the period between 1994 and 2014, found a negative relationship between the two. Ozurumba [ 23 ], in his study of Nigerian commercial banks, concluded that the non-performing loans had an adverse impact on the profitability of banks for the period between 2000 and 2013. A study by Ozgur and Gorus [ 22 ] using data for Turkish banks for the period from 2006 to 2016 reported a negative relationship between non-performing loans and bank profitability. Previous studies have used the following dependent and explanatory variables for the empirical analysis.

Profitability

In the literature, usually the Return on Assets (ROA) is taken as a proxy for profitability, which measures the percentage of profits that a bank earns with respect to its total assets [ 15 , 17 , 27 ]. We have used ROA as a proxy for profitability as it reflects the average asset value during a fiscal year [ 15 ].

Bank specific determinants of profitability

Net Non-Performing Advances (NNPA) : The higher the portion of income generating assets among total bank assets, the higher would be the interest income of the banks. When NPAs increase, the proportion of interest earning assets falls, which leads to a fall in interest income, and hence ROA declines. Thus, NPAs and ROA have a negative relation; as NPA rises, return on assets (ROA) of banks falls [ 5 ]. Masood and Ashraf [ 19 ] and Berger and Young [ 7 ] have used non-performing loans to total assets as a measure of non-performing assets.

Deposits are the principal and the cheapest source of funds for banks. Therefore, the more deposits a bank collects, higher will be the availability of funds for generating loans and for other profitable uses such as investments, higher will be the bank profitability. Thus, a positive relationship between deposits and profitability is expected [ 20 , 25 ].

Non-interest income

The non-interest income is the income of banks from sources other than interest bearing assets. It is an indicator of bank’s off-balance sheet business and fee income, that is non-traditional activities. Non-interest income consists of commission, service charges, and fees, guarantee fees, net profit from sale of investment securities, and foreign exchange profit. Higher the bank’s non-interest income, higher will be the profits [ 30 ] (Harbi 2019). We have used the ratio of non-interest income to total income as the variable for non-interest income.

Interest Income: Net Interest Margin (NIM)

Interest income is the difference between the interest rate a bank pays to its depositors and the interest rate it charges to its borrowers. It is measured as a ratio of Net Income to Total Assets. NIM represents income of the banks from its ‘core lending business’. NIM is adversely affected by NPAs, because when an asset becomes an NPA, it stops generating interest income and hence, interest earned by banks reduces, while the bank still has to pay interest on deposits [ 5 ]. The profitability of a bank increases with increase in net interest earning.

Capital adequacy

High capital reserve requirement leads to higher profitability for banks because of lower costs of financial risk for banks. Lower financial risks attract higher deposits and boost the banking busies, thereby leading to higher rate of profit. Several studies have found a positive relation between capital and profitability of banks [ 1 , 6 , 11 , 19 ] (Harbi 2019). We have used Tier 1 capital ratio as prescribed by the Basel Committee as the variable for capital adequacy.

Operating costs

It is the total amount of wages and salaries of bank employees and the cost of running branch office facilities. Higher the operating costs, lower will be the profits. Sufian and Habibullah [ 30 ] used the ratio of overhead expenses to total assets as a measure of overhead expenses. Al-Homaidi et al. [ 1 ] used ratio of operating expenses to interest income as a measure of operating efficiency and argued that lower the ratio, higher will be the management efficiency and higher will be the profits of banks, whereas Kohlscheen et al. [ 17 ] took the ratio of operational expenses to gross revenues as the measure of operating efficiency.

Macroeconomic determinants of profitability

Gdp growth rate.

It is the value of all final goods and services produced in a country in a given period of time. During higher economic growth, profitability of banks would be higher because it encourages banks to lend more and charge higher interests [ 11 , 30 ].

It is the rate at which general price level of goods and services rises and the purchasing power of currency falls. Studies have found that profitability of banks will be higher with inflation. It has been used by prior studies on banks’ profitability [ 1 , 9 , 11 , 19 ].

Interest rate

There has been mixed evidence with respect to the relationship between interest rate and profitability. Low interest rates along with stiff competition among banks put pressure on interest margins of banks and hence negatively affect bank profitability (Trujillo-Ponce 2013). Studies such as Demirguç-Kunt and Huizinga [ 11 , 29 ], Bourke [ 8 ] have found a positive relationship between interest rates and bank profitability. The repo rate has been used as it reflects the lending rate of banks.

There are very few studies that cover current phase of NPAs with the revised definition while analysing the NPAs and profitability in Indian banks. The present study not only covers the recent phase of NPAs crisis, but also covers the time period with revised or new definition of NPAs. The definition of NPAs in the present study follows uniformity.

In this study, we have drawn a sample of 39 scheduled commercial banks, out of which 20 are Public sector Banks (PSBs) and 19 are domestic private banks. As per the recent data, these 39 banks constitute more than 90 percent of the banking operation in terms of assets, and close to 95 percent in terms of deposits and credit disbursement in India. In case of Public Sector Banks (PSBs), the overall management responsibility lies with the Government, as it remains the majority stakeholder. The PSBs are governed by specific acts (banking acts) passed by the parliament. On the other side, the private banks are registered under the Companies Act and governed as per that act. Their management lies with the majority promoters or shareholders. In terms of NPA volume, it is largely the PSBs and some private banks that have been badly affected by the NPA crisis. Few small private banks were dropped from the analysis due to unavailability of data. The time period of the study is from 2005 to 2019. The period of the study has been chosen as the definition of NPA underwent a change in 2004, and the NPA data from 2005 onward follow uniformity with the new definition. Annual data for the sample of 39 banks was collected from a Reserve Bank of India (RBI) publication—Statistical Tables Relating to Banks in India. The bank specific determinates or factors that potentially explain the profitability of banks were obtained the above report. The data for macroeconomic variables were collected from the Handbook of Statistics on Indian Economy—a publication of the RBI.

In this study, we have estimated the determinants of profitability of Indian Scheduled Commercial Banks. The dependent variable is profitability, which is determined by a set of bank specific and macroeconomic factors (Table 1 ). In the study, the Return on Assets (ROA) has been used as the variable for profitability. In literature, the ROA is widely used as indicator or proxy for bank profitability. It is an appropriate indicator of profitability, as it measures the earnings of a bank in relative to its total assets. Therefore, it has been used as the dependent variable. We have used the following bank specific explanatory variables like Net NPA, total deposit, interest income, non-interest income, operational efficiency and capital adequacy. The study has used the following macroeconomic predictors of bank profitability—economic growth, inflation and interest rate to estimate the determinants of profitability.

To understand how NPAs impact the profitability, we have estimated the determinants of profitability of Indian scheduled commercial banks. We have employed the panel data estimation procedure to estimate the factors that have affected the profitability of banks in India. The following functional relationship has been employed to analyse the determinants of profitability.

where i  = bank, 1,….0.39, and t  = time, 1,….,15. \({\varepsilon }_{i,t}\) is the error term.

In the above equation, six bank specific factors and three macro-economic factors combined determine the profitability of a bank. In the paper, we have employed both the fixed and random effect approach to estimate the determinants of bank profitability. By using fixed effect (FE) model, the impact of variables those are time variant can be analysed. The FE estimation also controls for all time invariant heterogeneity among the sample banks. It therefore is likely to produce unbiased coefficient estimates due to omitted time invariant characteristics [ 31 ]. The general form of the fixed effects model can be expressed in the following equation [ 32 ].

In Eq. ( 2 ), the dependent variable ‘profitability’ is \({P}_{i,t}\) for i-th bank and t -th year. The dependent variable \({P}_{i,t}\) is determined by a set of exogenous regressor that includes both the bank specific and macroeconomic variables, \({X}_{i,t}\) , for i -th bank and t -th year; and \(\beta s\) are model parameters. Beta value in regression is the estimated coefficients of the independent or explanatory variables. It indicates a change in the dependent variable as a result of a unit change in explanatory variables keeping other independent or explanatory variables constant. The unobserved individual bank effect is \({\mu }_{i}\) , and the random error is, \({u}_{i,t}\) .

Unlike the fixed effects model, in the random effects (RE) model, it is assumed that the error term is uncorrelated with the explanatory variables. It allows the time invariant variables to act as similar to the predictors in the model. The benefit of RE is that the inferences can be generalised, beyond the sample drawn in a model [ 31 ]. The general form of the RE model can be expressed in the following equation [ 32 ].

In Eq. ( 3 ), the random error, \({\varepsilon }_{i,t}\) is with in entity error term and \({u}_{i,t}\) is between entity error term. \(\mu\) is the bank specific random effect. Random effect model assumes that the unobservable individual-specific effects (unobserved heterogeneity) are distributed independently of the explanatory variables or independent variables. More clearly, it assumes that the unobserved heterogeneity is uncorrelated with each explanatory variable across in all time period. Then, if the random effect model is significant, it indicates that the unobserved individual (cross-sectional) effects are uncorrelated with all the explanatory variables across all time-period.

The following fixed effects (FE) model has been estimated to analyse the determinants of profitability.

where i  = bank, 1,….0.39, and t  = time, 1,….,15.

In Eq. ( 4 ), the dependent variable is \(\text{ROA}_{i,t}\) . It is determined by a set of exogenous regressors that includes both the bank specific and macroeconomic variables. The unobserved individual bank effect is \({\mu }_{i}\) , and random error is \({u}_{i,t}\) . It is assumed that the set of explanatory variables is uncorrelated with the error term \({u}_{i,t}\) , and the error term is normally distributed, \({u}_{i,t}\) ~ N (0, \({\sigma }_{u}^{2}\) ), where \({\sigma }_{u}^{2}\) is > 0.

We have estimated the following random effect (RE) model to analyse the determinants of profitability in Indian scheduled commercial banks.

The descriptive statistics of the variables that has been used in the estimation of determinants of profitability is presented in Table 2 . The descriptive statistics of both the dependent and explanatory variables for the time period between 2005 and 2019 is presented in the form of mean, standard deviation, minimum and maximum. The results show that the return on profitability (ROA) ranges from − 5.49 to 2.13, with a mean ROA value of 0.65. Similarly, the minimum and maximum values of the explanatory variables range low to high. The mean and standard deviation values of the variables suggest that there is variation between the two.

The correlation matrix with correlation coefficients of the variables used is presented in Table 3 . The results suggest that there is no multicollinearity problem in the data. The results show a negative association of ROA with NNPA and CapT1. The rest of the explanatory variables exhibit positive association with ROA.

We have estimated both the fixed effect (Eq.  4 ) and random effect (Eq.  5 ) models to analyse the determinants of profitability in Indian scheduled commercial banks. The estimation result of the FE model shows that there is an inverse relationship between the rate of profit (ROA) and non-performing loans (NNPA), and the association is statistically significant (Table 4 ). Non-interest income (NII), interest income (II), capital adequacy (CAPT1) and GDP growth (GDPGr) are found to be positively associated with the rate of profit (ROA). The estimates are found to be statically significant. Ratio of operating cost to interest income (OCTII) shows negative relationship with profitability (ROA). The other macroeconomic variables like rate of inflation and interest rate show negative and positive associations, respectively. However, their association is not statistically significant.

The regression estimates of the RE model also give a similar result (Table 3 ). NPAs and operating cost (OCTII) are negatively associated with the rate of profit (ROA). Their relationship is statistically significant. On the other side, deposit (lnTD), non-interest income (NII), interest income (II), capital adequacy (CAPT1) and GDP growth (GDPGr) exhibit positive association with profitability (ROA). Their association is statistically significant. The other two macroeconomic explanatory variables, the rate of inflation and interest rate exhibit negative and positive associations, respectively. While total deposit was found to be significant in RE, it is found to be insignificant in FE model. In order to arrive at an appropriate test between FE and RE, the Hausman test was conducted. The results of Hausman test suggest that the RE estimate will be appropriate for the sample as the ‘ p ’ value is greater than 0.05 (Table 5 ).

In this paper, we have examined the impact of NPAs on the profitability of Indian banks. Using set of bank specific and macroeconomic variables, we have estimated the determinants of profitability of 39 commercial banks in India. The estimation result suggests that growing incidence of NPA is likely to reduce the profitability of the banks considerably. Results also suggest that increase in operating cost has negative impact on the profitability in Indian banks. The negative association between profitability (ROA) and NPA (NNPA); and profitability (ROA) and operating cost (OCTII) is statistically significant. The results show that there is a positive relationship between profitability (ROA), and interest earning (II) and non-interest earnings (NII). Their association is found to be statistically significant. The results further show that the volume of deposit (lnTD) is positively associated with the profitability (ROA). As financial intermediaries, commercial banks largely relay on interest earnings as their major source of income. In order to boost up their interest earnings, the banks must reduce their NPA volumes. The result suggests that Indian banks must reduce NPAs and operating cost in order to enhance their profitability.

The findings of the empirical estimation are similar to the findings of the studies by Kannan et al. [ 16 ], Sensarma and Ghosh [ 26 ], and Sinha and Sakshi [ 28 ]. A study by Kannan et al. [ 16 ], using data for 86 Indian banks, for the period from 1995–96 to 1999–2000 found that banks with higher NPAs have relatively lower profit margins. A study by Sensarma and Ghosh [ 26 ] of Indian commercial banks, for the period from 1997–98 to 2000–01, reported that a rise in NPA adversely affects the interest margins for banks and hence reduces bank profitability. Similarly, Sinha and Sakshi [ 28 ], in their study of 42 Indian commercial banks for the period from 2000 to 2013, found that higher credit risk, as measured by provision non-performing assets, negatively impacts bank profitability. Analysing NPAs in 46 Indian commercial banks from 2007 to 2014, Bawa et al. [ 5 ] found a negative relationship between NPAs and return on assets.

The paper has empirically estimated the factors that determine the profitability of Indian scheduled commercial banks, in order to understand the relationship between increasing non-performing advances and the rate of profit. The determinants of profitability have been estimated by taking a set of bank specific and macroeconomic explanatory variables. From the panel data estimation of 39 Public Sector and private banks, we found that the increase in non-performing advances has negative impact on the rate of profit. Operating cost is also found to be negatively associated with profitability. The estimates of both the FE and RE model suggest that non-interest income, interest income, capital adequacy and GDP growth rate have positively contributed to the rate of profit of the Indian banks. Given that, banks to a large extent depend on the performance of their loan assets as a critical source of income and profit, the rising NPAs is a cause of concern. It on the one hand reduces their interest earning and on the other side also affects their future deposits and increases their operating cost as the cost of recovery of NPAs will go up. The study suggests that the banks must reduce their NPAs and operating cost to improve their profitability.

Limitation of the study and future research avenues

The findings of the study are based on a sample of banks that mostly covers the PSBs and the private banks, covering the time period from 2005 to 2019. Though data for the year 2020 are available, it could not be incorporated due to recent bank mergers in India. Between 2020 and 2021, several mergers took place within the Public Sector Banks (PSBs). Post-merger, the number of PSBs has declined from 20 to 12. While it would be interesting to include the mergers into the empirical analysis, however one year is a too short time period to make any meaningful conclusion. The effect of merger in the analysis of NAPs and profitability of banks can be studied in future, with the availability of data for a longer time period.

Availability of data and materials

The data that support the findings of this study are collected from public domain resources. It is available at https://dbie.rbi.org.in/DBIE/dbie.rbi?site=publications [RBI publications/database on Indian economy].

Abbreviations

Non-Performing Assets

Gross Domestic Product

Fixed Effects

Random Effects

Reserve Bank of India

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The paper is drawn from a research project “Performance of India’s Banking Sector: A Critical Focus on Non-Performing Advances (NPAs)”, funded by the Indian Council of Social Science Research under ICSSR-MHRD IMPRESS Scheme. The funding body has NO role in designing of the study, analysis, interpretation of the data and in writing. The research paper/study has been designed and prepared by the authors.

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Both the authors have contributed in completing the research paper/study. The paper was conceptualised by SKD. The structure of the paper was prepared by SKD in consultation with KU. KU largely contributed to the literature section and data collection. Estimation and analysis were done by SKD. Both the authors have contributed to the methodology section. Both the authors have read and approved the final manuscript.

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Das, S.K., Uppal, K. NPAs and profitability in Indian banks: an empirical analysis. Futur Bus J 7 , 53 (2021). https://doi.org/10.1186/s43093-021-00096-3

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Coronavirus pandemic impact on bank performance

1 School of Economics, Shandong University of Finance and Economics (SDUFE), Jinan, Shandong, China

2 Youth League Committee of Shandong University of Finance and Economics, (SDUFE), Jinan, Shandong, China

Mohsin Shabir

3 School of International Trade and Economics, Shandong University of Finance and Economics (SDUFE), Jinan, Shandong, China

Associated Data

Publicly available datasets were analyzed in this study. This data can be found at: Bankscope Database, World Governance Indicators, and World Development Indicators.

This study examines the effects of the coronavirus (COVID-19) epidemic on the performance of the banking sector. Our sample consists of 1,575 banks in 85 countries from 2020Q1 to 2021Q4. The findings demonstrate that the COVID-19 outbreak has significantly decreased bank performance. Moreover, the adverse impact of COVID-19 on the bank’s performance depends on the bank’s and country-specific aspects. The adverse effect of the COVID-19 outbreak on bank performance is higher in smaller, undercapitalized, and less diversified banks. At the same time, a better institutional environment and financial development have significantly increased the strength and resilience of banks. The results are quite robust to using the alternative bank performance measures and estimation techniques. These findings provide practical implications for regulators and policymakers in the face of unprecedented uncertainty caused by COVID-19 epidemics.

JEL Classification: G01, G21, L50.

Introduction

In early December 2019, Wuhan City, China, first observed the beginning of the novel coronavirus (COVID-19; Demir and Danisman, 2021 ; Padhan and Prabheesh, 2021 ). Since its origin in Wuhan, COVID-19 has wreaked havoc worldwide because it is a highly transmittable and pathogenic viral infection ( Zaremba et al., 2020 ). Countries including China, Italy, Spain, France, the United Kingdom, and the United States have been hit hard by the severe COVID-19 outbreak ( Padhan and Prabheesh, 2021 ; Song et al., 2021 ). Therefore, on March 11, 2020, the World Health Organization (WHO) stated that COVID-19 is a global pandemic outbreak and is considered a “once-in-a-century pathogen” for the following reasons. Firstly, COVID-19 is perhaps a unique outcome in terms of its global scope as a pandemic. This disease’s rapid transmission rate shows that COVID-19 is more dangerous than any other pandemic ( Padhan and Prabheesh, 2021 ). Secondly, the mortality risk of COVID-19 is 1%, which is worse than normal influenza because it can kill healthy and older people. This fatality risk is comparable to the 1,857 influenza pandemic (0.6%) and the 1,918 Spanish flu (2%). However, due to the absence of pharmaceutical innovations, the actual death rate of COVID-19 is unpredictable ( Padhan and Prabheesh, 2021 ).

The outbreak of COVID-19 brought seriously affected health care, economy, transportation, and other fields in different industries and regions ( Shen et al., 2020 ). Moreover, the COVID-19 pandemic has reverberated across economies and financial markets and greatly impacted real economic activity. The economic impact of COVID-19 can be generally divided into two aspects: supply and demand impacts. The supply impact is the outcome of reductions in working hours and aggregate demand resulting from reduced incomes due to unemployment related to the lockdown. However, Maliszewska et al. (2020) highlight the four key channels through which a pandemic affects the economy. First, it directly impacts through the decrease in employment, which drives a reduction in the demand for capital, leading to a loss in output. Second, the rise in transaction costs increases the costs of imports and exports for goods and services, resulting in a drop in trade and productivity. Third, with a sharp reduction in travel, governments have imposed several restrictions to reduce infections that disrupt international tourism and lead to lower incomes and loss of productivity. Finally, the declined in demand for services. The demand seems to have taken a big hit, as these emergency shutdowns have also locked households into their homes, dramatically reducing consumer spending.

However, this ongoing COVID-19 has taken significant losses for countless businesses, leading to serious disruptions in various industries ( Khlystova et al., 2022 ). A growing body of literature observes the potential impact of the COVID-19 pandemic on different aspects. In this regard, one group of researchers focuses on the short-term effect of COVID-19 on stock market returns or volatility. They show that COVID-19 significantly reduces stock market returns and increases stock market volatility ( Al-Awadhi et al., 2020 ; Baek et al., 2020 ; Zaremba et al., 2020 ; Harjoto et al., 2021 ). At the same time, the other group of researchers has investigated the COVID-19 pandemic’s impact on the firm’s financial performance in various sectors ( Shen et al., 2020 ; Hu and Zhang, 2021 ; Atayah et al., 2022 ; Wellalage et al., 2022 ). Shen et al. (2020) show that firm performance worsens during the COVID-19 pandemic, which is more significant when a firm’s sales revenue or investment scale is low. At the same time, Hu and Zhang (2021) reported that the adverse effect of COVID-19 on firm performance is less pronounced in countries with better institutional environments, well-developed financial systems, and better health care systems.

While this unexpected shock is likely to impact banks, little is yet known about how it might affect the resilience and performance of the banking system as a whole ( Goodell, 2020 ; Duan et al., 2021 ). This is because the bank generally faces a broader range of risks compared to other financial institutions and is more closely connected with economic agents’ day-to-day activities ( Barua and Barua, 2020 ). Banks traditionally deal with a wide range of risks. The pandemic is set to exacerbate them through liquidity shortages, credit reduction, falling returns from investments, and rises in non-performing loans and default rates ( Barua and Barua, 2020 ; Goodell, 2020 ). This may be worse in nations where banks support millions of individuals and firms with comparatively low financial and economic capacity under a weak policy environment and high market competition ( Barua and Barua, 2020 ).

Coronavirus can affect banks in different ways. For example, banks worldwide hold large US dollar-denominated borrowings to fund international trade and financial investments ( Aldasoro and Ehlers, 2018 ). Financial crises tighten the money markets that lend dollars, implying risks for the global banking system. However, as a first response to the pandemic, central banks stretched current swap lines and formed new lines to reduce the cost of dollar funding ( Bahaj and Reis, 2020 ; Demir and Danisman, 2021 ). Bank prudential regulatory actions, such as relaxing the treatment of non-performing loans and reducing capital buffers, mitigate the adverse effect of COVID-19 on the financial system’s stability ( Demir and Danisman, 2021 ; Bitar and Tarazi, 2022 ). Danisman et al. (2021) presented that equity markets in countries with more strict regulatory requirements on capital and liquidity are more resilient to COVID-19. However, due to Basel III capital and liquidity reforms since 2008, banks are well situated to engage the extreme effect of COVID-19. At the same time, facilitation of the behavior of non-performing loans and capital buffers during a pandemic can put banks’ solvency at risk. The possibility of an increase in non-performing loans and substantial withdrawal of deposits by firms and households will adversely affect the performance of banks ( Danisman et al., 2021 ; Goodell, 2020 ). Besides, COVID-19 could adversely affect the efficiency of firms across all businesses, and there could be spillover effects on banks, which would increase their exposure to credit risk. This would threaten their stability and create some obstacles to future intermediation with some potential spillovers to the real economy ( Demir and Danisman, 2021 ).

This study contributes to the literature in the following aspects. First, prior literature examined the impact of COVID-19 on macroeconomic prospectives, such as economic growth ( Apergis and Apergis, 2021 ), International Trade ( Gruszczynski, 2020 ; Vidya and Prabheesh, 2020 ), oil price ( Mensi et al., 2020 ; Gharib et al., 2021 ), and gold price ( Mensi et al., 2020 ). While at the firm level, most existing studies focus on the impact of COVID-19 on a firm’s performance ( Fu and Shen, 2020 ; Gu et al., 2020 ; Shen et al., 2020 ; Xiong et al., 2020 ; Škare et al., 2021 ) and stock market returns and volatility ( Al-Awadhi et al., 2020 ; Baek et al., 2020 ; Zaremba et al., 2020 ; Harjoto et al., 2021 ). At the same time, the impact of COVID-19 on bank performance is scarce. In this study, we shifted the perspective to bank performance at the international level. Secondly, we explored the mechanisms through which COVID-19 affects bank performance. Lastly, as COVID-19 spreads globally, we determine whether the impact of COVID-19 on bank performance varies with institutional quality and level of financial development.

This study examines how the COVID-19 outbreak impacts the banking sector’s performance worldwide. Our sample comprises 1,575 listed and unlisted banks in 85 countries from 2020Q1 to 2021Q4. We use numerous alternative bank performance measures for a comprehensive examination and robustness. The findings illustrate that COVID-19 has significantly decreased bank performance. Moreover, the COVID-19 epidemic’s effects on the bank’s performance vary in the bank’s and country-specific aspects. The finding shows that adverse effects of COVID-19 on bank performance are more pronounced in smaller, undercapitalized, and less diversified banks. Also, a better institutional environment and financial development diminish the negative effects of COVID-19 on banks’ performance. Our primary results continue across alternative model specifications (i.e., GMM).

The rest of the paper is organized as follows. The section “Literature review” provides an overview of the relevant literature. The section “Data and methodology” defines our sample, study variables, econometric model, and summary statistics. The sections “Results and discussion” and “Robustness checks” report the empirical outcomes and robustness tests, respectively. The section “Conclusion” presents the conclusion of the paper.

Literature review

Coronavirus is a major health emergency worldwide. The outbreak of COVID-19 has severely affected healthcare, economy, transportation, and other sectors in various industries and regions ( Padhan and Prabheesh, 2021 ). This increasing pandemic in emerging and developed countries has led to strict lockdowns and unprecedented economic activity disruptions ( Baldwin and di Mauro, 2020 ; Padhan and Prabheesh, 2021 ). For example, in the second quarter of 2020, global GDP fell by more than 4.9 percent due to economic disruption ( Padhan and Prabheesh, 2021 ). The deterioration in the international trade in goods and services was possibly greater than during the global financial crisis of 2007–08 ( IMFC, 2020 ). Therefore, due to weak supply and demand, international trade was constricted by 3.5 percent in the second quarter of 2020 ( Vidya and Prabheesh, 2020 ). A rapid drop in consumption of goods and services was observed due to a sharp drop in income and weak consumer confidence. Moreover, emerging countries experienced substantial capital outflows and reduced investment and productivity due to the pandemic ( BIS, 2019 ; Padhan and Prabheesh, 2021 ).

Moreover, prior literature analyzed the COVID-19 impact in several ways. For example, Choi (2020) and Njindan Iyke (2020) stated that due to COVID-19, production and credit were reduced. Bauer and Weber (2020) , Liu et al. (2020) , and Yu et al. (2020) proved a significant reduction in consumption, investment, and labor force participation rate. Moreover, COVID-19 has unfavorably affected corporate performance ( Gu et al., 2020 ; Shen et al., 2020 ) and herding behavior ( Espinosa-Méndez and Arias, 2020 ). Additionally, some researchers have explored the impact of COVID-19 on the price of oil ( Fu and Shen, 2020 ; Narayan, 2020 ). They highlighted that the deterioration in oil prices due to the pandemic unfavorably influenced the energy sector’s performance. Fu and Shen (2020) and Narayan (2020) argued that COVID-19 increased oil price volatility and negatively affected energy industries,

The COVID-19 pandemic also amplified worldwide financial risks and destructively affected international financial markets ( Al-Awadhi et al., 2020 ; Cao et al., 2020 ; Harjoto et al., 2020 ). COVID-19 has adversely impacted the stock market in the form of ambiguity and a decline in global stock returns, decreasing capital inflows, and creating constraints on investment, new project financing, and accessibility to liquidity in the international financial system ( Padhan and Prabheesh, 2021 ). Guedhami et al. (2021) found that international firms experienced considerably lower stock prices than domestic companies during the pandemic crisis. They also demonstrate that the better financial system of the country moderates these negative performance impacts while real characteristics increase negative crisis returns. Al-Awadhi et al. (2020) and Wang and Enilov (2020) show that COVID-19 significantly reduces stock market returns. Zaremba et al. (2020) illustrate that COVID-19 led to a significant rise in stock market volatility.

Additionally, COVID-19 has a devastating effect on the efficiency of firms across all businesses and may have spillover effects on banks, increasing their exposure to credit risk. Acharya and Steffen (2020) revealed that the increased pace of reducing credit growth, particularly by riskier companies, could damage bank balance sheets and lesser their capital adequacy ratios. This would threaten their stability and create obstacles to upcoming intermediation by possible spillovers to the real economy. Li et al. (2020) reported that U.S. banks significantly amplified their lending if they had more idle loan commitments at the start of the pandemic. However, they argued that though banks improved their credit growth, their total credit supply remained unchanged. In the same way, Greenwald et al. (2020) show that U.S. banks that experienced large credit line drawdowns were more restrictive in lending to small firms during the COVID-19 crisis. Beck and Keil (2021) demonstrate that U.S. banks faced increasing loan loss provisions and non-performing loans. Hasan et al. (2021) illustrate that the spread of syndicated loans increased as the lender or borrower became more vulnerable to epidemics. Ҫolak and Öztekin (2021) and Duan et al. (2021) investigate the effect of the pandemic on global lending and banks’ systemic risk from an international perspective. Demir and Danisman (2021) show that stock returns of banks with a large size, lesser non-performing loans, well capitalization, and higher deposits are more resilient to the pandemic. Dursun-de Neef et al. (2022) show that worse-capitalized banks increased their loan supply significantly more during the pandemic. Elnahass et al. (2021) show that COVID-19 significantly affects financial performance over various financial performance and stability measures. Demirgüç-Kunt et al. (2021) argued that liquidity assistance, borrower support programs, and monetary easing moderated the negative effects of the crisis, but their effects varied significantly across banks and countries. Therefore, based on this evidence in this study, we will find the impact of COVID-19 on bank performance.

Data and methodology

Data and sample selection.

To analyze the impact of COVID-19 on the banking sector, we obtained quarterly balance sheet data of 1,575 listed and unlisted banks in 85 different countries from the Bankscope database for 2020Q1 to 2021Q4. 1 Quarterly frequency data are preferred for the following reasons: (a) The most important reason is that daily and monthly data are unavailable for financial and accounting data; (b) the COVID-19 period covers only two quarters. Hence, our frequency is driven by current financial and accounting data availability in 2020–21. Country-related variables such as GDP per capita , inflation, and bank concentration are taken from IMF and World Bank. Table 1 reports a detailed explanation of all variables and sources. Table 2 displays the summary statistics of the variables of interest.

Variables definition.

This table presents detailed descriptions of study variables.

Descriptive statistics.

This table shows summary statistics for the variables used in this study.

Measurements of variables

Bank performance measurement.

Although banking institutions have become gradually complex, profitability is the underlying driver of bank performance. In this study, we used the two accounting-based measures that are widely used in the earlier literature ( Adesina, 2021 ; Dang and Dang, 2021 ; Elnahass et al., 2021 ) as a dependent variable to evaluate the bank’s performance. These accounting-based measures return on average total assets (ROAA) and return on average equity (ROAE). These are the banking sector’s most accepted financial performance measures ( Simpson and Kohers, 2002 ). Moreover, we also used several alternative proxies of bank performance as robustness.

COVID-19 indicators

In this study, we follow Ҫolak and Öztekin (2021) and use the total number of COVID-19 confirmed cases per million in the country as a proxy for COVID-19.

Bank and country-specific variables

In addition to COVID-19, we have incorporated numerous bank-related and country-related control variables in our model to address the potential omitted variables problem. The bank-related control variables are bank size (SIZ), capitalization (CAP), liquidity (LIQ), asset structure (LTA), and bank diversification (DIV). Bank size (SIZE) is measured through the natural logarithm of a bank’s total assets. Capitalization (CAP) is estimated as equity to total assets. Bank liquidity (LIQ) is calculated as the ratio of liquidity assets to total assets. Net loan to total assets (LTA) is used as the bank’s asset structure proxy. Bank diversification (DIV) is measured by the non-interest income ratio to net operating income. At the same time, the country-related control variables are GDP per capita (GDPpc), inflation (INF) and bank concentration (CON). We use GDP per capita and inflation rates to control business cycles’ overall effects, unobserved factors that vary across countries ( Wu et al., 2020 ). Finally, bank Concentration (CON) controls the country’s market structure.

Empirical framework

In this study, to examine the impact of the COVID-19 pandemic on bank performance, our baseline model is as follows

where I , j t indicate the bank, country and quarter (time). BP denotes our dependent variables bank performance, which is measured as ROAA and ROAE. COVID-19 is our primary explanatory variable measured as the total number of COVID-19 confirmed cases per million in the country. X i t is a vector of our bank-related control variables. Z j t is a vector of country and market structure control variables. β, γ , a n d δ are the parameters of the model. Moreover, μ i , and ʎt are the bank and time effects and ε i t is the error term. We estimate equations (1) with the fixed-effects model. 2

Results and discussion

Baseline regression results.

Our core objective of the study is to determine the possible impacts of the COVID-19 pandemic on bank performance across countries. For this purpose, we regresses the bank performance on COVID19 and show our baseline regression model results in Table 3 . In columns (1) and (5) in Table 3 , we analyze the impact of COVID-19 on bank performance along cross-sectional and time fixed-effects, but we do not incorporate bank and country-related control variables. In columns (2) and (6), we contain the bank-related control variable, while in columns (3) and (7); we comprise country-related control variables. In columns (4) and (8), we incorporate all bank and country-related control variables with cross-sectional and time fixed-effects to examine the impacts of COVID-19 on bank performance. Overall, our findings highlight the significant negative effects of COVID-19 on bank performance in the sampling countries. Columns (4) and (8) in Table 3 show that COVID-19 coefficients are statistically significant with a negative sign with both ROAA and ROAE of bank performance measures. This finding is consistent with Elnahass et al. (2021) and shows that the outbreak of COVID-19 has significantly decreased the banking sector’s profitability. To simplify the economic interpretation of the regression coefficients of our key variables of interest, we implement a log transformation to COVID-19. The coefficient of COVID-19 reflects the βCOVID-19% change in bank performance for a 1% change in the number of disease cases per million.

COVID-19 impact on bank performance.

This table shows the results of the baseline regression on analyzing the effect of the COVID-19 epidemic on bank performance. The sample comprises 1,575 banks in 85 countries from 2020 Q1 to 2021 Q4. Our dependent variable is bank performance measured as ROA and ROE. COVID-19 is our primary explanatory variable of interest, measured as the total number of COVID-19 confirmed cases per million in the country. Robust standard errors are reported in parentheses.

This finding can be interpreted as the spread of the virus forcing governments to initiate several preventive measures, such as social distancing, lockdowns, and business shutdowns ( Duan et al., 2021 ). These activities, in turn, lead to adverse economic impacts on firms and households. As a result, firms have experienced significant declines in revenue and increased cost, and households have experienced job losses and income declines ( Duan et al., 2021 ). Therefore, firms and households may not be able to service their debt, increasing the probability of default ( Bartik et al., 2020 ). These effects are likely to spread to banks, resulting in lost revenue and a surge in non-performing loans, negatively affecting banks’ profits, capital, and solvency ( Beck and Keil, 2021 ). Furthermore, lower demand for bank services may result in lower non-interest income, lowering bank profitability and performance ( Beck and Keil, 2021 ).

Regarding the first set (bank-specific) of control variables, we find that the bank size (SIZE) coefficients are statistically significant and positively linked with ROAA and ROAE. This result aligns with earlier studies by Adesina (2021) and Dang and Dang (2021) and shows that large banks have high ROAA and ROAE. Similarly, capitalization (CAP) also significantly positive impacts ROAA and ROAE. These outcomes supported the empirical finding of Chortareas et al. (2012) and Adesina (2021) , suggesting that better-capitalized banks are highly efficient than those with a lower capital base. Also, the coefficients of asset structure (LTA) significantly positively impact ROAA and ROAE, demonstrating that a better bank asset structure enhances bank profitability. Lastly, bank diversification is also positively associated with ROAA and ROAE. These results support the bank’s diversification advantage and show that reliance on sources of non-interest income enhances the bank’s profits.

In contrast, regarding the country-related control variables. This outcome indicates greater concentration enhances the banking sector’s performance and efficiency. The GDP per capita coefficients show a significant positive relationship with bank performance. At the same time, the estimated inflation coefficients show an adverse and highly significant relationship in all bank performance measures. The bank concentration coefficient is positively linked with ROAA and ROAE.

Bank heterogeneity

Furthermore, we extend our basic analysis to examine how bank characteristics shape the effects of COVID-19 shocks on bank performance. Current studies have shown that bank features such as size, capitalization, liquidity, and diversification have significantly influenced bank performance ( Altunbas et al., 2012 ; Shabir et al., 2021 ). Therefore, to estimate the heterogeneity across the bank, we include the interaction terms of bank size, capitalization, liquidity, and diversification with COIVD-19 in our main regression model. The results are reported in Table 4 . The outcomes show that the coefficients on the interaction term of COVID-19 with size, liquidity, and diversification are positive and statistically significant on ROAA and ROAE. At the same time, the coefficient of interactions of COVID-19 with capitalization is significantly negative with all bank performance measures in ROAA and ROAE. These findings indicate that large size, more liquid and well-diversified banks reduce the adverse impact of COVID-19 on bank performance. In contrast, the poorly capitalized bank increases the adverse impact of COVID-19 on bank performance.

Role of bank heterogeneity.

This table demonstrates how a bank with diverse characteristics responds to the COVID-19 epidemic. The sample comprises 1,575 banks in 85 countries from 2020 Q1 to 2021 Q4. Our dependent variable is bank performance measured as ROA and ROE. COVID-19 is our primary explanatory variable of interest, measured as the total number of COVID-19 confirmed cases per million in the country. Robust standard errors are reported in parentheses.

COVID-19 and bank performance: Role of the institutional quality

The quality of institutions plays an important role during the financial crisis ( Klomp and De Haan, 2014 ; Fazio et al., 2018 ). Numerous recent studies have shown that various aspects of the formal and informal institutional environment significantly affect a bank’s profitability and risk levels. Beck et al. (2006) show that the regulatory policies and institution quality are significantly related to the banking system’s stability. Klomp and De Haan (2014) highlight that tight regulatory policy and higher supervision power reduce bank risk. To capture the institutional quality, we followed the previous literature and used Worldwide Governance Indicators (WGI), which contains six different aspects of institutional quality (i.e., government effectiveness, political stability, regulatory quality, control of corruption, the rule of law, and accountability). Therefore, for a more comprehensive analysis of COVID-19 and bank performance nexus, we include the interaction terms of COVID-19 with institutional quality in the regression model (1). The results are presented in Table 5 , which shows that the coefficients of the institutional quality variable and their interaction terms with COVID-19 are significantly positive at different levels. This indicates that better institutional quality increases the bank’s performance in response to COVID-19 epidemics.

Bank performance during the COVID-19 pandemic. The role of the institutional quality.

The role of the institutional quality. This table presents the country’s institutional quality role during the COVID-19 pandemic. The sample comprises 1,575 banks in 85 countries from 2020 Q1 to 2021 Q4. Our dependent variable is bank performance measured as ROA and ROE. COVID-19 is our primary explanatory variable of interest, measured as the total number of COVID-19 confirmed cases per million in the country. We used the Worldwide Governance Indicators (WGI) to capture the institutional quality, which contains six different aspects of institutional quality such as government effectiveness (GEF), political stability (PST), regulatory quality (RQL), control of corruption (COC), the rule of law (RUL), and voice and accountability (VOA). Robust standard errors are reported in parentheses.

COVID-19 and bank performance: Role of financial development

Additionally, prior empirical and theoretical literature highlight that the development of the financial sector has a positive influence on economic activity by increasing the performance of financial services, capital allocation, technological innovation, the efficiency of resource distribution, risk management, and reducing the risk of crises ( Levine, 1997 ; Vithessonthi and Tongurai, 2016 ). Therefore, we further examine whether the financial development of a country’s banking system mitigates the pandemic’s adverse effect on bank performance. For this reason, we use the financial development index (FDI) from IMF, which summarizes how developed the financial institution is in terms of its depth (FID), access (FIA), and efficiency (FIE). The finding is reported in Table 6 , which shows that the coefficient of COVID-19 remains significantly negative in ROAA and ROAE. At the same time, the coefficient for all financial development measures and their interaction terms are significantly positive with ROAA and ROAE. These findings consistently show that banks in countries with more financial development are less vulnerable to COVID-19 shocks on bank performance than in other countries.

Bank performance during the COVID-19 pandemic The role of financial development.

The role of financial development. This table reports the role of financial development during the COVID-19 pandemic. The sample comprises 1,575 banks in 85 countries from 2020 Q1 to 2021 Q4. Our dependent variable is bank performance measured as ROA and ROE. COVID-19 is our primary explanatory variable of interest, measured as the total number of COVID-19 confirmed cases per million in the country. We have taken the four different indexes from IMF to capture the financial development of a country, such as financial development index (FDI), financial institution depth (FID), financial institution access (FIA), and financial institution efficiency (FIE). Robust standard errors are reported in parentheses.

Robustness checks

Alternative dependent variable.

It is challenging to evaluate and capture a bank’s overall performance using a single measure ( Lee et al., 2014 ; Baselga-Pascual and Vähämaa, 2021 ). Therefore, as robustness, we further examine whether our main findings hold when we use alternative measures of bank performance. For this purpose, we followed existing literature ( Liang et al., 2013 ; Adesina, 2021 ; Dang and Huynh, 2022 ) and used the net interest margin ratio (NIM), cost-to-income ratio (CIN), and non-performing loans (NPL) as an alternative measure of for bank performance. The results are reported in Table 7 , which shows that COVID-19 coefficients remain statistically significant with a negative (positive) sign in NIM (CIN and NPL) of bank performance measures. 3 This finding shows an adverse impact of COVID-19 on bank performance remains consistent with the previous findings in Table 3 .

Alternative dependent variable.

This table presents the result of our baseline regression models by using the net interest margin ratio (NIM), cost-to-income ratio (CIN), and non-performing loans (NPL) as alternative measures of bank performance. The sample comprises 1,575 banks in 85 countries from 2020 Q1 to 2021 Q4. COVID-19 is our primary explanatory variable of interest, measured as the total number of COVID-19 confirmed cases per million in the country. Robust standard errors are reported in parentheses.

Alternative methodology

Our model may have possible endogeneity issues due to reverse causality, omitted variable, and control variable. Therefore, we employ the generalized method of moments (GMMs) and use the two-step system estimator with adjusted standard error for potential heteroskedasticity proposed by Blundell and Bond (1998) as robustness to test our main outcomes are sensitive to estimation approaches. The technique accounts for the unobserved heterogeneity and the dynamic nature of panel data. Moreover, it is more appropriate to deal with possible endogeneity issues and is highly reliable even in reverse causality, omitted variables, and measurement errors ( Bond and Hoeffler, 2001 ). Table 8 describes the results of the System GMM. We find that our baseline finding in Table 3 is still consistent even though we are considering unobserved heterogeneity, simultaneity, and dynamic endogeneity.

Alternative methodology.

This table expresses the impact of the COVID-19 epidemic on bank performance by using the System GMM. The sample comprises 1,575 banks in 85 countries from 2020 Q1 to 2021 Q4. Our dependent variable is bank performance measured as ROA and ROE. COVID-19 is our primary explanatory variable of interest, measured as the total number of COVID-19 confirmed cases per million in the country. Robust standard errors are reported in parentheses.

Coronavirus is not just a global epidemic and public health crisis. There is a widespread consensus among economists that this has devastatingly impacted the global economy. The economic damage led by the COVID-19 epidemics is mainly due to the reductions in income, productivity, unemployment increase, and trade disruptions. This study investigates how the COVID-19 outbreak affects the banking sector’s performance worldwide. Our sample comprises 1,575 listed and unlisted banks in 85 countries from 2020Q1 to 2021Q4. We use numerous alternative bank performance measures for a comprehensive examination and robustness. The findings illustrate that the outbreak of COVID-19 has significantly reduced bank performance.

We also determine whether the COVID-19 epidemic’s influence on the bank’s performance depends on the bank’s and country-specific aspects. For this reason, we find that bank performance is most negatively affected by the COVID-19 outbreak in smaller, undercapitalized, and less diversified banks. Moreover, we find a better institutional environment and financial development diminish the negative effects of COVID-19 on banks’ performance. Our primary results continue across alternative model specifications (i.e., GMM). The current study’s findings have important policy implications for researchers, policymakers, regulators, and financial institutions to manage risks within and across countries. As policy implications, the study suggests that the government should provide larger economic support, loosened capital requirements, and adjust insolvency rules to mitigate the negative impact of COVID-19. The current study’s major limitation is related to the small number of banks in our sample. We use data from only 1,575 banks whose quarterly data are available. Therefore, if our sample size were larger, more favorable results may have emerged. In future research directions, this study can be further expanded by comparing the impact of COVID-19 on Islamic versus conventional banks.

Data availability statement

Author contributions.

All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.

This work was sponsored by the Natural Science Foundation of Shandong Province, China (grant number: ZR2021MG004) and the Youth Entrepreneurship Talent Introduction and Education Team of Colleges and Universities in Shandong Province, China.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

1 We choose this sample of banks because of the quarterly availability of data on the Bankscope database.

2 Hausman test suggests that the fixed-effects estimator is more appropriate compared to the random-effects estimator in our study.

3 Note that in Table 7 , the bank performance measure variables such as cost-to-income ratio (CIN), and non-performing loans (NPL) are calculated in such a way that increases the variables indicates lower bank performance and increase the risk. In the interest margin ratio (NIM) case, a higher value shows more bank profitability.

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  1. Role of Commercial Banks in Economic Development Free Essay Example

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  2. (PDF) SERVICE QUALITY IN COMMERCIAL BANKS: A STUDY OF PUBLIC SECTOR

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  1. Financial technology and the future of banking

    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. ... investment, or commercial banking. The service sub-groupings include payments, settlements, fund management, trading, treasury ...

  2. Banks & Banking: Articles, Research, & Case Studies on Banks & Banking

    Between 2008 and 2014, the Top 4 banks sharply decreased their lending to small business. This paper examines the lasting economic consequences of this contraction, finding that a credit supply shock from a subset of lenders can have surprisingly long-lived effects on real activity. 26 Jun 2017. Working Paper Summaries.

  3. Big Data Applications the Banking Sector: A Bibliometric Analysis

    Therefore, the value and usage of big analytics have proved to be vital, especially regarding marketing improvement in commercial banks and risk management performance . However, the ... Table 2 lists down names of researchers who contributed high-impact research papers on big data in banking. The data in the table is sorted according to the ...

  4. A literature review of risk, regulation, and profitability of banks

    This study presents a systematic literature review of regulation, profitability, and risk in the banking industry and explores the relationship between them. It proposes a policy initiative using a model that offers guidelines to establish the right mix among these variables. This is a systematic literature review study. Firstly, the necessary data are extracted using the relevant keywords ...

  5. Applications of Artificial Intelligence in commercial banks

    Core business area Description of business area IT systems used in business area; Lending: A loan is a type of financial claim to the payment of a future sum of money and/or a periodic payment of money (Casu et al., 2016).• IS from external vendors (i.e., SAP (SAP, 2019a) "Commercial Banking Operations" Module) Commercial banks lend money to their customers in several ways, such as ...

  6. Stay Competitive in the Digital Age: The Future of Banks, WP/21/ ...

    The Future of Banks by Estelle Xue Liu IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.

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    The annual production of scientific publications on banking efficiency is presented in Fig. 2.The first research article related to banking performance was published by Fraser and Rose [], who studied the effect of new bank appearance in the market on bank performance.The annual growth of publications on banking performance or banking efficiency is recorded to 12.39%.

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    The research paper applies a quantitative approach to empirically examine the possible nexus between internal control and credit risk. ... banks, 28 joint-stock commercial banks, nine full-foreign owned banks, two joint-venture banks. However, this research only focuses on the commercial banks, so the sample size used for the regression model ...

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    The present research paper provides empirical evidence on the interconnection between credit risk and bank-specific/internal factors on FP commercial banks. To analyze the data set, first, the study applies the descriptive analysis to identify the big picture of the data, then the correlation section and at the end, regression results are ...

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    by Juliane Begenau and Tim Landvoigt. This study at the intersection of macroeconomics and banking explores the optimal regulation of banks. Studying and quantifying the effects of capital requirements in a model that features regulated (commercial) and unregulated (shadow) banks, the authors find that a higher capital requirement makes ...

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    The scientific construction of the Internet finance index is an important prerequisite for empirically testing the impact of Internet finance on commercial banks. This paper draws on the research results of Shen and Guo (2015) and uses the Baidu search index to build an Internet finance index to measure the development of Internet finance. 3.2 ...

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    Therefore, the research on credit risk of commercial banks has important theoretical and practical significance. This article mainly predicts the credit risk of corporate customers of commercial banks by constructing Artificial Neural Network model. ... In this paper, the first layer of the activation function is selected as the hyperbolic ...

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    Service quality is the extent of the difference between the desires of customers and their experiences of the services they received (Gronroos, 1990). Srinivas and Rao (2018) advocated that banks ...

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    The purpose of this paper is to examine and analyze the performance of Nepalese commercial banks through cash flow ratios. Descriptive as well as analytical research design has been used. All the government owned commercial banks has been taken as the sample of the study using judgmental technique.

  18. THE ROLE OF COMMERCIAL BANKS IN THE DEVELOPMENT OF ECONOMY

    PDF | On Nov 30, 2016, Mohsin Hassan Alvi published THE ROLE OF COMMERCIAL BANKS IN THE DEVELOPMENT OF ECONOMY | Find, read and cite all the research you need on ResearchGate

  19. Analyzing Performance of Banks in India: A Robust Regression ...

    This research aims to analyze the impact of bank performance determinants on bank performance by applying robust regression analysis. For this, the relationship between return on assets and net interest margin with bank performance determinants has been discussed using robust regression. ... Commercial banks are classified as "public sector ...

  20. NPAs and profitability in Indian banks: an empirical analysis

    As financial intermediaries, the commercial banks to a large extent depend on the performance of their lending as a critical source of earning. Due to increasing loan failures, the share of non-performing advances has increased substantially in recent years, thereby adversely impacting their profitability. The paper has examined the NPAs and profitability relationship by estimating the ...

  21. Research on Competitive Strategy of Commercial Banks under the

    This paper conducts research on competitive strategy of commercial banks under the background of internet finance to realize better operation, enhance industrial competition, achieve sustainable and stable development. The internet brings much convenience to the development of financial industry and enhances financial operation efficiency. By aid of network technology, the financial industry ...

  22. PDF Role Of Commercial Banks In Economic Growth And Development: A

    Commercial banking institutions are vital for the smooth running and functioning of the financial systems. Banks serve as repositories and custodians of very important financial information. ... Factors on Profitability of Commercial Banks: A Case Study of Pakistan. Research Journal of Finance and Accounting, 4 (2), 117- 126. [4]. Chimkono, C ...

  23. PDF Commercial banking top trends for 2024

    Focusing on operational and regulatory fundamentals. With global GDP growth expected to decelerate from 2.7% in 2023 to 2.4% in 2024,1 eficiency and cost optimization are top of mind for most commercial banks. Trends 1 to 3 in our report focus on how commercial banks are preparing for economic uncertainty.

  24. Coronavirus pandemic impact on bank performance

    The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ... (2001). GMM estimation of empirical growth models. CEPR Discussion Papers/Centre for Economic Policy Research Discussion Papers. ... Federal Reserve Bank of san ...

  25. Just Transition: Issues for Central Banks and Financial Regulators

    Share. Abstract: Recent calls on central banks and financial regulators to use the tools at their disposal to help mitigate the negative economic and social impacts of climate policies are based on several false analogies between the energy transition and the "just" energy transition. The same false analogies explain why voluntary efforts ...

  26. Negative Interest Rates and Corporate Tax Behavior in Banks

    WU International Taxation Research Paper Series No. 2021-09, TRR 266 Accounting for Transparency Working Paper Series No. 63, Rotman School of Management Working Paper No. 3921343 ... our results suggest that the increased costs associated with NIRs are borne by commercial banks which lead to an increase in their respective tax planning ...

  27. NYCB issues spark concern for banks' commercial real estate exposure

    For banks or bank-holding companies with more than $750 billion in assets, those loans grew to 1.94% of commercial real estate loans as of the end of the third quarter in 2023, the highest point ...

  28. PDF Bank of Japan Working Paper Series

    Introduction. This paper analyzes the sources of high inflation in Japan since the COVID-19 pandemic by applying the small-scale economic model proposed by Bernanke and Blanchard (2023, hereafter BB) to the Japanese economy. This work is part of a joint project by Ben Bernanke, Olivier Blanchard, and economists from ten central banks: the Bank ...

  29. The Impact of Digital Transformation on Customer Satisfaction to

    Search 216,895,107 papers from all fields of science. Search. ... The Impact of Digital Transformation on Customer Satisfaction to Digital Banking Service of Commercial Banks in Vietnam @article{2023TheIO, title={The Impact of Digital Transformation on Customer Satisfaction to Digital Banking Service of Commercial Banks in Vietnam}, author ...