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Risk Management in the Banking Sector

Introduction.

Like other businesses, Banks face multiple risks. However, due to the strategic importance of the banking sector to the economy and the government’s involvement in risk control, risk management in banking is heavier than in other industries. Understanding the real risk is critical to effective risk management. The main types of risks that banks face include credit risk, market risk, operational risk, reputational risk, and liquidity risk (Vyas & Singh, 2011). Understanding the specific risks facing a particular bank allows the management to apply various tools to effectively manage them. According to Kanchu & Kumar (2013), various models and tools are used to manage the different types of risks in the banking sector. For example, the Value at Risk Model and Stress Testing Model are widely used tools to minimize the impact of unfavorable events in the banking sector. However, each of these models has its own merits and drawbacks. This discussion analyzes the problems associated with the use of the Value at Risk Model and Stress Testing Model in managing risks in the banking sector. In addition, the discussion covers the problems associated with credit risk in measuring risk. The discussion further recommends the various ways of eliminating or reducing problems associated with the Value at Risk Model, Stress Testing Model, and Credit Risk.

Problems and Issues Associated with the Use of Value at Risk Model

By definition, the Value at Risk Model is a metric used to estimate the risk of an investment in the financial sector. According to Krause (2003), it is a statistical tool that measures the amount of potential loss that could occur over a specific period of time. The model specifically calculates the probability of losing more than a specified amount in a portfolio. This model is widely used by banks due to its simplicity. Although this model is a useful risk management metric particularly when applied appropriately, it is prone to significant measurement errors (Krause, 2003). Some of the main problems and issues associated with this model include difficulties in calculating risk for large portfolios, differences in approaches, the inability to measure worst-case loss, and giving a false sense of security (Berkowitz & O’Brien, 2002). In addition, the outcome of the Value at Risk Model is as good as the assumptions and inputs used.

Difficulties in Calculating for Large Portfolios

Calculating the Value at Risk of a portfolio not only requires the estimation of the return and volatility of individual securities but also the correlation coefficient between them (Krause, 2003). Given the growing demand for large and diversified portfolios, using the Value at Risk model to measure risk becomes extremely difficult. Thus, the higher the number of securities in a portfolio, the more difficult it is to estimate the Value at Risk.

Differences in Approaches

There are generally three methods of calculating Value at Risk of a portfolio. These methods include the historical approach, variance-covariance approach, and the Monte Carlo Simulation approach (Hendricks, 1996). Using these different methods to calculate Value at Risk of the same portfolio always leads to different results.

Inability to Measure Worst Case Loss

It is impossible to know the maximum possible loss by simply looking at the Value at Risk. For example, the worst-case loss may only be just a small percentage slightly higher than the Value at Risk, but it could be significant enough to affect the investment (Krause, 2003). Simply put, Value at Risk does not reveal anything about the maximum possible loss.

It Gives Some False Sense of Security

Using Value at Risk to assess risk exposure can be misleading. The vast majority consider Value at Risk to be the most they can lose, particularly when it is calculated with a confidence level of 99% (Berkowitz & O’Brien, 2002). The 1% loss can be so significant and this is what causes misunderstanding. The 99% confidence level in the Value at Risk model can consciously or unconsciously give people some false sense of security.

The Results are as Good as the Assumptions and Inputs Used

Like other quantitative techniques used in finance, the results of Value at Risk are as effective as the inputs and assumptions used (Berkowitz & O’Brien, 2002). For example, a common mistake when using the variance-covariance approach is the assumption of normal distribution for assets and returns from portfolios. Inputting unrealistic return distributions can result in the underestimation of risk.

Overall, Value at Risk is not always a good tool for risk measurement because it is vulnerable to significant measurement errors. However, it can be an effective risk management tool when applied properly with a clear understanding of its underlying assumptions.

Problems and Issues Associated with the Use of Stress Testing Model

The Stress Testing Model is a risk management tool that involves computer-generated and highly complicated simulation models that analyze the impacts of extreme scenarios (Stein, 2012). Simply put, the stress testing model analyzes how a financial institution’s balance sheet responds to certain situations. For instance, in times of financial uncertainties, banks and other financial institutions deploy stress testing models to study the market and analyze portfolio risk to help these institutions make an informed decision based on the results. These models rely on high-quality data to help organizations effectively identify potential risks and mitigate them. According to Basel II regulations, the purpose of stress testing in the banking sector is to establish whether a bank has sufficient capital and liquid assets to withstand stressful times (Stein, 2012). It is carried out for internal risk management as well as for regulatory purposes. UK, USA, and EU regulators, for instance, require banks to perform specific stress tests. Financial institutions are required to provide a capita plan justifying the models used as well as the results of their stress testing. If a bank does not meet the stress test requirements because of insufficient capital, then it must raise more capital by limiting the payment of dividends. Although stress tests are effective risk management tools, banks face various challenges when implementing stress testing models. According to Thun (2012), these challenges include determining how and what needs to be stressed, designing effective and meaningful scenarios, Gathering sufficient data, and communicating the results for action.

Determining How and What Needs to Be Stressed

Many banks experience problems with this initial step of determining what should be stressed. Instead of determining what to be stressed from a bank’s market and risk analysis perspective, many organizations align their efforts with market best practices or standard regulatory requirements (Thun, 2012). For example, over the last two decades, two main methods of stress testing have gained more appeal, including scenario analysis and sensitivity tests. Sensitivity tests are criticized because they assume that only a single factor like the shift in the yield curve change. Sensitivity tests, on the other hand, are easy and straightforward to execute but are criticized because they do not consider the interdependence between the risk factors.

Designing Meaningful and Effective Scenarios

A major challenge with stress testing models is designing meaningful and effective scenarios. Based on the scenario designed, the outcome of the stress test could significantly misrepresent the risks that a bank is actually exposed to (Battiston & Martinez-Jaramillo, 2018). The scenario may not be plausible or severe enough, thus it may not mitigate the bank against critical risks. Classic examples of this form of misrepresentation are the unforeseen problems that befell Franco-Belgian Bank in October 2011 and the sudden problems that the bank of Ireland faced in 2010 despite having passed stress tests outlined by the European Banking Authority regulators.

Gathering Sufficient Data

The greatest challenge that banks face is the lack of sufficient data. Specifically, data from periods of severe stress is not always available (Fell, 2006). Such information would be the basis for designing meaningful scenarios as well as understanding the link between risk drivers and macroeconomic variables. Given the relationship between various macroeconomic variables like inflation, GDP, oil prices, and GDP, gathering sufficient information allows banks to model behavior in times of stress (Fell, 2006). On the contrary, the lack of sufficient data about the macroeconomic variables leads to an unstable and weak relationship between the scenarios designed and the relevant risk factors.

Communicating the Results for Action

Efforts to design meaningful and effective stress tests are useless if communication, which is a critical aspect is missing. Internal communication, which should be in the format prescribed by the regulator is as important as external communication. For effectiveness, the stress test must be communicated clearly to everyone internally (Battiston & Martinez-Jaramillo, 2018). In addition, the stress test must be well understood, particularly by the senior managers. Furthermore, the stress tests must indicate the degree of risk exposure and the impact on the business.

Overall, even though much has been achieved over the last few decades in terms of designing an effective stress test framework, risk managers still face several challenges as mentioned above. These challenges must be addressed in order to turn stress testing models into powerful risk management tools.

Issues Associated with Credit Risk in Measuring Risks in Banking

With the recent global crisis, credit risk management remains a top priority for many banks and regulatory authorities. Although strict credit requirements like the top-down approach have been useful in mitigating economic risk, such approaches have left many financial organizations struggling to achieve effective credit risk assessment (Brown & Moles, 2014). In an attempt to implement a raft of risk strategies to improve overall performance and gain a competitive advantage, banks have to overcome a number of credit risk management challenges. According to Altman (2002), some of the main challenges associated with credit risk management include, ineffective data management, limited group-wide risk infrastructure, inefficient risk tools, and less-intuitive reporting.

Ineffective Data Management

Effective credit management requires an organization to securely gather data, analyze it, and store it securely based on a particular criterion. All databases need to be regularly updated to ensure easier retrieval as well as to avoid relying on outdated information in making decisions (Altman, 2002). Streamlining the manner in which data is gathered, analyzed, stored, and retrieved is critical to effective credit risk management. Data centralization is also important as it allows for easier analysis and modeling, which provides a clearer picture of a business or individual’s credit worthiness.

Limited Group-wide risk Infrastructure

Most often, it is not enough to evaluate the risk posed by a single individual or entity. A comprehensive and broader view of all risk measures is key to understanding the risk of a new borrower. In addition, having efficient stress testing models and capabilities is also important to ensure that an organization has an accurate assessment of risks (Cumming & Hirtle, 2001). Various rating agencies are also important as far as establishing credit scores for individuals is concerned. Banks can utilize a credit rating score system to determine default risk and make accurate credit decisions. Thus, having a 360-degree view of a financial organization’s risk that covers the entire group can create new opportunities for lending while maintaining risk at lower levels.

Insufficient Risk Tools

A broader and comprehensive scorecard should identify potential strengths and weaknesses linked to a loan. Risk analytics, for instance, took a step forward when financial institutions, particularly the bigger banks began embracing big data programs (Altman, 2002). However, small and medium banks experienced a slower adoption because of the huge investment required. With the modern risk tools being made available in the market, banks of all sizes will have access to big data in the near future. Risk analytics is considered a huge transformation that allows banks and other lending institutions to align their culture, governance, strategies, and technology in order to optimize risk management. Through optimization, banks can mitigate the risk management process, thus broadening their returns from lending.

Less-intuitive Reporting

In order to provide valuable insights, financial reports should be presented in a clear, clean, visualized, and intuitive manner. For example, eliminating irrelevant data from financial statements that only overburden analysts can help in narrowing down to the most pertinent information (Cumming & Hirtle, 2001). Using analytics requires a powerful reporting process that allows the bank to clearly visualize at the individual borrower level as well as across the organization. Thus, the lack of clear, clean, visualized, and intuitive reporting is a major hindrance to effective credit risk management.

Ways of Reducing or Eliminating Problems Raised in Part A

The various problems and issues associated with the use of the Value at Risk Model, Stress Testing Model, and credit risk can be minimized or eliminated through a range of ways as outlined below:

Addressing Issues Associated with Value at Risk Model

Despite its underlying limitations and problems, the Value at Risk Model can still be useful as long as the users understand its weaknesses. As a recommendation, Value at Risk should just be complemented with other risk management tools, particularly those that take into account the 1% worst-case that the Value at Risk Model ignores completely (Choudhry, 2011). Thus, banks should not allow the Value at Risk Model to become a false sense of security.

Addressing Issues Associated with Stress Testing Model

The results of stress testing can significantly affect the process of decision-making. For this reason, there is a need to benchmark stress test results against a bank’s appetite. Benchmarking allows a bank to review its underlying risk profile. The bank management has the responsibility of preparing strategic plans for early interventions. These strategic plans include raising more funds, suspending the payment of dividends to the shareholders, and eliminating or minimizing some business activities (Vyas & Singh, 2011). In addition, banks can ensure frequent reporting by incorporating in the company’s strategic planning the outcomes of hypothetical stress tests and scenarios, including those that are not likely to materialize.

Addressing Issues Associated with Credit Risk

The various challenges in using credit risk to evaluate risks in banking can be overcome through close monitoring, mitigation, creating relationships between different risks, and regular reporting. Mitigation involves reducing or minimizing the likelihood of uncertain events occurring (Kanchu & Kumar, 2013). Credit risk should be continually reviewed to ensure that the bank is adequately protected. Regular monitoring should be made a routine and a proactive process. Close monitoring allows banks to address emerging trends in order to establish whether or not progress is being made in mitigating credit risk. Furthermore, creating relationships between different risks, mitigation activities, and business units provide a cohesive picture of a bank (Kanchu & Kumar, 2013). These relationships help in the recognition of downstream and upstream dependencies, as well as the identification of other risks. Designing centralized controls further eliminates the probability of missing important pieces of information. Frequent reporting involves presenting regular updates regarding the way the risk management program is progressing. The information should be reported in a clear and engaging manner to attract the support of different stakeholders at the bank. Developing regular risk reports that give a dynamic view and centralize all information is step forward towards giving a broader view of the bank’s risk profile.

In summary, whether a bank is managing risks as defined by the UK, USA, EU, or other regulatory authorities, it is important to consider risk management as more than just a compliance requirement. The different risk models are designed to address the unique needs of every bank, as well as the dynamic needs of the banking industry. The Value at Risk Model and Stress Testing Model alone is not enough to sufficiently manage all risks a bank faces. To be effective, these models should be complemented with other risk management tools. In addition, understanding the weaknesses of each model helps the management to use every model selectively.

Altman, E. I. (2002). Managing credit risk: A challenge for the new millennium.  Economic Notes ,  31 (2), 201-214.

Battiston, S., & Martinez-Jaramillo, S. (2018). Financial networks and stress testing: Challenges and new research avenues for systemic risk analysis and financial stability implications.  Journal of Financial Stability ,  35 , 6-16.

Berkowitz, J., & O’Brien, J. (2002). How accurate are value‐at‐risk models at commercial banks?.  The journal of finance ,  57 (3), 1093-1111

Brown, K., & Moles, P. (2014). Credit risk management.  K. Brown & P. Moles, Credit Risk Management ,  16 .

Choudhry, M. (2011).  An introduction to banking: liquidity risk and asset-liability management . John Wiley & Sons.

Cumming, C., & Hirtle, B. (2001). The challenges of risk management in diversified financial companies.  Economic policy review ,  7 (1).

Fell, J. (2006, November). Overview of stress testing methodologies: from micro to macro. In  Powerpoint Presentation at the Korea Financial Supervisory Commission/Financial Supervisory Service-International Monetary Fund Seminar on Macroprudential Supervision Conference: Challenges for Financial Supervisors, Seoul, November  (Vol. 7).

Hendricks, D. (1996). Evaluation of value-at-risk models using historical data.  Economic policy review ,  2 (1).

Kanchu, T., & Kumar, M. M. (2013). Risk management in banking sector–an empirical study.  International journal of marketing, financial services & management research ,  2 (2), 145-153.

Krause, A. (2003). Exploring the limitations of value at risk: how good is it in practice?.  The Journal of Risk Finance .

Stein, R. M. (2012). The role of stress testing in credit risk management.  Journal of investment management ,  10 (4), 64.

Thun, C. (2012). Common Pitfalls in Stress Testing.  Moody’s Analytics .

Vyas, M., & Singh, S. (2011). Risk Management in Banking Sector.  BVIMR Management Edge ,  4 (1).

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How Banks Can Finally Get Risk Management Right

• James C. Lam

SVB was a cautionary tale.

Banks have three lines of defense for managing risk — and then regulators are the fourth line of defense. In the case of Silicon Valley Bank, all four failed. If banks want to manage risk better, one good place to start is making sure a Chief Risk Officer is in place and a board-level risk committee is in place. And the people on that committee should have real experience in managing enterprise risk.

Here we go again. Banks ought to have the best risk management. But whatever safeguards were in place didn’t prevent Silicon Valley Bank from failing, destroying over $40 billion in shareholder value, and forcing unprecedented government intervention to protect depositors.  

• JL James C. Lam  is President, James Lam & Associates, a risk management consulting firm. Previously, he served as Partner of Oliver Wyman and chief risk officer at Fidelity Investments and GE Capital Market Services. He is the author of  Implementing Enterprise Risk Management: From Methods to Applications .

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• Open access
• Published: 03 September 2022

A literature review of risk, regulation, and profitability of banks using a scientometric study

• Shailesh Rastogi 1 ,
• Arpita Sharma 1 ,
• Geetanjali Pinto 2 &
• Venkata Mrudula Bhimavarapu   ORCID: orcid.org/0000-0002-9757-1904 1 , 3  

Future Business Journal volume  8 , Article number:  28 ( 2022 ) Cite this article

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2 Citations

Metrics details

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 from the Scopus database. The initial search results are then narrowed down, and the refined results are stored in a file. This file is finally used for data analysis. Data analysis is done using scientometrics tools, such as Table2net and Sciences cape software, and Gephi to conduct network, citation analysis, and page rank analysis. Additionally, content analysis of the relevant literature is done to construct a theoretical framework. The study identifies the prominent authors, keywords, and journals that researchers can use to understand the publication pattern in banking and the link between bank regulation, performance, and risk. It also finds that concentration banking, market power, large banks, and less competition significantly affect banks’ financial stability, profitability, and risk. Ownership structure and its impact on the performance of banks need to be investigated but have been inadequately explored in this study. This is an organized literature review exploring the relationship between regulation and bank performance. The limitations of the regulations and the importance of concentration banking are part of the findings.

Introduction

Globally, banks are under extreme pressure to enhance their performance and risk management. The financial industry still recalls the ignoble 2008 World Financial Crisis (WFC) as the worst economic disaster after the Great Depression of 1929. The regulatory mechanism before 2008 (mainly Basel II) was strongly criticized for its failure to address banks’ risks [ 47 , 87 ]. Thus, it is essential to investigate the regulation of banks [ 75 ]. This study systematically reviews the relevant literature on banks’ performance and risk management and proposes a probable solution.

Issues of performance and risk management of banks

Banks have always been hailed as engines of economic growth and have been the axis of the development of financial systems [ 70 , 85 ]. A vital parameter of a bank’s financial health is the volume of its non-performing assets (NPAs) on its balance sheet. NPAs are advances that delay in payment of interest or principal beyond a few quarters [ 108 , 118 ]. According to Ghosh [ 51 ], NPAs negatively affect the liquidity and profitability of banks, thus affecting credit growth and leading to financial instability in the economy. Hence, healthy banks translate into a healthy economy.

Despite regulations, such as high capital buffers and liquidity ratio requirements, during the second decade of the twenty-first century, the Indian banking sector still witnessed a substantial increase in NPAs. A recent report by the Indian central bank indicates that the gross NPA ratio reached an all-time peak of 11% in March 2018 and 12.2% in March 2019 [ 49 ]. Basel II has been criticized for several reasons [ 98 ]. Schwerter [ 116 ] and Pakravan [ 98 ] highlighted the systemic risk and gaps in Basel II, which could not address the systemic risk of WFC 2008. Basel III was designed to close the gaps in Basel II. However, Schwerter [ 116 ] criticized Basel III and suggested that more focus should have been on active risk management practices to avoid any impending financial crisis. Basel III was proposed to solve these issues, but it could not [ 3 , 116 ]. Samitas and Polyzos [ 113 ] found that Basel III had made banking challenging since it had reduced liquidity and failed to shield the contagion effect. Therefore, exploring some solutions to establish the right balance between regulation, performance, and risk management of banks is vital.

Keeley [ 67 ] introduced the idea of a balance among banks’ profitability, regulation, and NPA (risk-taking). This study presents the balancing act of profitability, regulation, and NPA (risk-taking) of banks as a probable solution to the issues of bank performance and risk management and calls it a triad . Figure  1 illustrates the concept of a triad. Several authors have discussed the triad in parts [ 32 , 96 , 110 , 112 ]. Triad was empirically tested in different countries by Agoraki et al. [ 1 ]. Though the idea of a triad is quite old, it is relevant in the current scenario. The spirit of the triad strongly and collectively admonishes the Basel Accord and exhibits new and exhaustive measures to take up and solve the issue of performance and risk management in banks [ 16 , 98 ]. The 2008 WFC may have caused an imbalance among profitability, regulation, and risk-taking of banks [ 57 ]. Less regulation , more competition (less profitability ), and incentive to take the risk were the cornerstones of the 2008 WFC [ 56 ]. Achieving a balance among the three elements of a triad is a real challenge for banks’ performance and risk management, which this study addresses.

Triad of Profitability, regulation, and NPA (risk-taking). Note The triad [ 131 ] of profitability, regulation, and NPA (risk-taking) is shown in Fig.  1

Triki et al. [ 130 ] revealed that a bank’s performance is a trade-off between the elements of the triad. Reduction in competition increases the profitability of banks. However, in the long run, reduction in competition leads to either the success or failure of banks. Flexible but well-expressed regulation and less competition add value to a bank’s performance. The current review paper is an attempt to explore the literature on this triad of bank performance, regulation, and risk management. This paper has the following objectives:

To systematically explore the existing literature on the triad: performance, regulation, and risk management of banks; and

To propose a model for effective bank performance and risk management of banks.

Literature is replete with discussion across the world on the triad. However, there is a lack of acceptance of the triad as a solution to the woes of bank performance and risk management. Therefore, the findings of the current papers significantly contribute to this regard. This paper collates all the previous studies on the triad systematically and presents a curated view to facilitate the policy makers and stakeholders to make more informed decisions on the issue of bank performance and risk management. This paper also contributes significantly by proposing a DBS (differential banking system) model to solve the problem of banks (Fig.  7 ). This paper examines studies worldwide and therefore ensures the wider applicability of its findings. Applicability of the DBS model is not only limited to one nation but can also be implemented worldwide. To the best of the authors’ knowledge, this is the first study to systematically evaluate the publication pattern in banking using a blend of scientometrics analysis tools, network analysis tools, and content analysis to understand the link between bank regulation, performance, and risk.

This paper is divided into five sections. “ Data and research methods ” section discusses the research methodology used for the study. The data analysis for this study is presented in two parts. “ Bibliometric and network analysis ” section presents the results obtained using bibliometric and network analysis tools, followed by “ Content Analysis ” section, which presents the content analysis of the selected literature. “ Discussion of the findings ” section discusses the results and explains the study’s conclusion, followed by limitations and scope for further research.

Data and research methods

A literature review is a systematic, reproducible, and explicit way of identifying, evaluating, and synthesizing relevant research produced and published by researchers [ 50 , 100 ]. Analyzing existing literature helps researchers generate new themes and ideas to justify the contribution made to literature. The knowledge obtained through evidence-based research also improves decision-making leading to better practical implementation in the real corporate world [ 100 , 129 ].

As Kumar et al. [ 77 , 78 ] and Rowley and Slack [ 111 ] recommended conducting an SLR, this study also employs a three-step approach to understand the publication pattern in the banking area and establish a link between bank performance, regulation, and risk.

Determining the appropriate keywords for exploring the data

Many databases such as Google Scholar, Web of Science, and Scopus are available to extract the relevant data. The quality of a publication is associated with listing a journal in a database. Scopus is a quality database as it has a wider coverage of data [ 100 , 137 ]. Hence, this study uses the Scopus database to extract the relevant data.

For conducting an SLR, there is a need to determine the most appropriate keywords to be used in the database search engine [ 26 ]. Since this study seeks to explore a link between regulation, performance, and risk management of banks, the keywords used were “risk,” “regulation,” “profitability,” “bank,” and “banking.”

Initial search results and limiting criteria

Using the keywords identified in step 1, the search for relevant literature was conducted in December 2020 in the Scopus database. This resulted in the search of 4525 documents from inception till December 2020. Further, we limited our search to include “article” publications only and included subject areas: “Economics, Econometrics and Finance,” “Business, Management and Accounting,” and “Social sciences” only. This resulted in a final search result of 3457 articles. These results were stored in a.csv file which is then used as an input to conduct the SLR.

Data analysis tools and techniques

This study uses bibliometric and network analysis tools to understand the publication pattern in the area of research [ 13 , 48 , 100 , 122 , 129 , 134 ]. Some sub-analyses of network analysis are keyword word, author, citation, and page rank analysis. Author analysis explains the author’s contribution to literature or research collaboration, national and international [ 59 , 99 ]. Citation analysis focuses on many researchers’ most cited research articles [ 100 , 102 , 131 ].

The.csv file consists of all bibliometric data for 3457 articles. Gephi and other scientometrics tools, such as Table2net and ScienceScape software, were used for the network analysis. This.csv file is directly used as an input for this software to obtain network diagrams for better data visualization [ 77 ]. To ensure the study’s quality, the articles with 50 or more citations (216 in number) are selected for content analysis [ 53 , 102 ]. The contents of these 216 articles are analyzed to develop a conceptual model of banks’ triad of risk, regulation, and profitability. Figure  2 explains the data retrieval process for SLR.

Data retrieval process for SLR. Note Stepwise SLR process and corresponding results obtained

Bibliometric and network analysis

Figure  3 [ 58 ] depicts the total number of studies that have been published on “risk,” “regulation,” “profitability,” “bank,” and “banking.” Figure  3 also depicts the pattern of the quality of the publications from the beginning till 2020. It undoubtedly shows an increasing trend in the number of articles published in the area of the triad: “risk” regulation” and “profitability.” Moreover, out of the 3457 articles published in the said area, 2098 were published recently in the last five years and contribute to 61% of total publications in this area.

Articles published from 1976 till 2020 . Note The graph shows the number of documents published from 1976 till 2020 obtained from the Scopus database

Source of publications

A total of 160 journals have contributed to the publication of 3457 articles extracted from Scopus on the triad of risk, regulation, and profitability. Table 1 shows the top 10 sources of the publications based on the citation measure. Table 1 considers two sets of data. One data set is the universe of 3457 articles, and another is the set of 216 articles used for content analysis along with their corresponding citations. The global citations are considered for the study from the Scopus dataset, and the local citations are considered for the articles in the nodes [ 53 , 135 ]. The top 10 journals with 50 or more citations resulted in 96 articles. This is almost 45% of the literature used for content analysis ( n  = 216). Table 1 also shows that the Journal of Banking and Finance is the most prominent in terms of the number of publications and citations. It has 46 articles published, which is about 21% of the literature used for content analysis. Table 1 also shows these core journals’ SCImago Journal Rank indicator and H index. SCImago Journal Rank indicator reflects the impact and prestige of the Journal. This indicator is calculated as the previous three years’ weighted average of the number of citations in the Journal since the year that the article was published. The h index is the number of articles (h) published in a journal and received at least h. The number explains the scientific impact and the scientific productivity of the Journal. Table 1 also explains the time span of the journals covering articles in the area of the triad of risk, regulation, and profitability [ 7 ].

Figure  4 depicts the network analysis, where the connections between the authors and source title (journals) are made. The network has 674 nodes and 911 edges. The network between the author and Journal is classified into 36 modularities. Sections of the graph with dense connections indicate high modularity. A modularity algorithm is a design that measures how strong the divided networks are grouped into modules; this means how well the nodes are connected through a denser route relative to other networks.

Network analysis between authors and journals. Note A node size explains the more linked authors to a journal

The size of the nodes is based on the rank of the degree. The degree explains the number of connections or edges linked to a node. In the current graph, a node represents the name of the Journal and authors; they are connected through the edges. Therefore, the more the authors are associated with the Journal, the higher the degree. The algorithm used for the layout is Yifan Hu’s.

Many authors are associated with the Journal of Banking and Finance, Journal of Accounting and Economics, Journal of Financial Economics, Journal of Financial Services Research, and Journal of Business Ethics. Therefore, they are the most relevant journals on banks’ risk, regulation, and profitability.

Location and affiliation analysis

Affiliation analysis helps to identify the top contributing countries and universities. Figure  5 shows the countries across the globe where articles have been published in the triad. The size of the circle in the map indicates the number of articles published in that country. Table 2 provides the details of the top contributing organizations.

Location of articles published on Triad of profitability, regulation, and risk

Figure  5 shows that the most significant number of articles is published in the USA, followed by the UK. Malaysia and China have also contributed many articles in this area. Table 2 shows that the top contributing universities are also from Malaysia, the UK, and the USA.

Key author analysis

Table 3 shows the number of articles written by the authors out of the 3457 articles. The table also shows the top 10 authors of bank risk, regulation, and profitability.

Fadzlan Sufian, affiliated with the Universiti Islam Malaysia, has the maximum number, with 33 articles. Philip Molyneux and M. Kabir Hassan are from the University of Sharjah and the University of New Orleans, respectively; they contributed significantly, with 20 and 18 articles, respectively.

However, when the quality of the article is selected based on 50 or more citations, Fadzlan Sufian has only 3 articles with more than 50 citations. At the same time, Philip Molyneux and Allen Berger contributed more quality articles, with 8 and 11 articles, respectively.

Keyword analysis

Table 4 shows the keyword analysis (times they appeared in the articles). The top 10 keywords are listed in Table 4 . Banking and banks appeared 324 and 194 times, respectively, which forms the scope of this study, covering articles from the beginning till 2020. The keyword analysis helps to determine the factors affecting banks, such as profitability (244), efficiency (129), performance (107, corporate governance (153), risk (90), and regulation (89).

The keywords also show that efficiency through data envelopment analysis is a determinant of the performance of banks. The other significant determinants that appeared as keywords are credit risk (73), competition (70), financial stability (69), ownership structure (57), capital (56), corporate social responsibility (56), liquidity (46), diversification (45), sustainability (44), credit provision (41), economic growth (41), capital structure (39), microfinance (39), Basel III (37), non-performing assets (37), cost efficiency (30), lending behavior (30), interest rate (29), mergers and acquisition (28), capital adequacy (26), developing countries (23), net interest margin (23), board of directors (21), disclosure (21), leverage (21), productivity (20), innovation (18), firm size (16), and firm value (16).

Keyword analysis also shows the theories of banking and their determinants. Some of the theories are agency theory (23), information asymmetry (21), moral hazard (17), and market efficiency (16), which can be used by researchers when building a theory. The analysis also helps to determine the methodology that was used in the published articles; some of them are data envelopment analysis (89), which measures technical efficiency, panel data analysis (61), DEA (32), Z scores (27), regression analysis (23), stochastic frontier analysis (20), event study (15), and literature review (15). The count for literature review is only 15, which confirms that very few studies have conducted an SLR on bank risk, regulation, and profitability.

Citation analysis

One of the parameters used in judging the quality of the article is its “citation.” Table 5 shows the top 10 published articles with the highest number of citations. Ding and Cronin [ 44 ] indicated that the popularity of an article depends on the number of times it has been cited.

Tahamtan et al. [ 126 ] explained that the journal’s quality also affects its published articles’ citations. A quality journal will have a high impact factor and, therefore, more citations. The citation analysis helps researchers to identify seminal articles. The title of an article with 5900 citations is “A survey of corporate governance.”

Page Rank analysis

Goyal and Kumar [ 53 ] explain that the citation analysis indicates the ‘popularity’ and ‘prestige’ of the published research article. Apart from the citation analysis, one more analysis is essential: Page rank analysis. PageRank is given by Page et al. [ 97 ]. The impact of an article can be measured with one indicator called PageRank [ 135 ]. Page rank analysis indicates how many times an article is cited by other highly cited articles. The method helps analyze the web pages, which get the priority during any search done on google. The analysis helps in understanding the citation networks. Equation  1 explains the page rank (PR) of a published paper, N refers to the number of articles.

T 1,… T n indicates the paper, which refers paper P . C ( Ti ) indicates the number of citations. The damping factor is denoted by a “ d ” which varies in the range of 0 and 1. The page rank of all the papers is equal to 1. Table 6 shows the top papers based on page rank. Tables 5 and 6 together show a contrast in the top ranked articles based on citations and page rank, respectively. Only one article “A survey of corporate governance” falls under the prestigious articles based on the page rank.

Content analysis

Content Analysis is a research technique for conducting qualitative and quantitative analyses [ 124 ]. The content analysis is a helpful technique that provides the required information in classifying the articles depending on their nature (empirical or conceptual) [ 76 ]. By adopting the content analysis method [ 53 , 102 ], the selected articles are examined to determine their content. The classification of available content from the selected set of sample articles that are categorized under different subheads. The themes identified in the relationship between banking regulation, risk, and profitability are as follows.

Regulation and profitability of banks

The performance indicators of the banking industry have always been a topic of interest to researchers and practitioners. This area of research has assumed a special interest after the 2008 WFC [ 25 , 51 , 86 , 114 , 127 , 132 ]. According to research, the causes of poor performance and risk management are lousy banking practices, ineffective monitoring, inadequate supervision, and weak regulatory mechanisms [ 94 ]. Increased competition, deregulation, and complex financial instruments have made banks, including Indian banks, more vulnerable to risks [ 18 , 93 , 119 , 123 ]. Hence, it is essential to investigate the present regulatory machinery for the performance of banks.

There are two schools of thought on regulation and its possible impact on profitability. The first asserts that regulation does not affect profitability. The second asserts that regulation adds significant value to banks’ profitability and other performance indicators. This supports the concept that Delis et al. [ 41 ] advocated that the capital adequacy requirement and supervisory power do not affect productivity or profitability unless there is a financial crisis. Laeven and Majnoni [ 81 ] insisted that provision for loan loss should be part of capital requirements. This will significantly improve active risk management practices and ensure banks’ profitability.

Lee and Hsieh [ 83 ] proposed ambiguous findings that do not support either school of thought. According to Nguyen and Nghiem [ 95 ], while regulation is beneficial, it has a negative impact on bank profitability. As a result, when proposing regulations, it is critical to consider bank performance and risk management. According to Erfani and Vasigh [ 46 ], Islamic banks maintained their efficiency between 2006 and 2013, while most commercial banks lost, furthermore claimed that the financial crisis had no significant impact on Islamic bank profitability.

Regulation and NPA (risk-taking of banks)

The regulatory mechanism of banks in any country must address the following issues: capital adequacy ratio, prudent provisioning, concentration banking, the ownership structure of banks, market discipline, regulatory devices, presence of foreign capital, bank competition, official supervisory power, independence of supervisory bodies, private monitoring, and NPAs [ 25 ].

Kanoujiya et al. [ 64 ] revealed through empirical evidence that Indian bank regulations lack a proper understanding of what banks require and propose reforming and transforming regulation in Indian banks so that responsive governance and regulation can occur to make banks safer, supported by Rastogi et al. [ 105 ]. The positive impact of regulation on NPAs is widely discussed in the literature. [ 94 ] argue that regulation has multiple effects on banks, including reducing NPAs. The influence is more powerful if the country’s banking system is fragile. Regulation, particularly capital regulation, is extremely effective in reducing risk-taking in banks [ 103 ].

Rastogi and Kanoujiya [ 106 ] discovered evidence that disclosure regulations do not affect the profitability of Indian banks, supported by Karyani et al. [ 65 ] for the banks located in Asia. Furthermore, Rastogi and Kanoujiya [ 106 ] explain that disclosure is a difficult task as a regulatory requirement. It is less sustainable due to the nature of the imposed regulations in banks and may thus be perceived as a burden and may be overcome by realizing the benefits associated with disclosure regulation [ 31 , 54 , 101 ]. Zheng et al. [ 138 ] empirically discovered that regulation has no impact on the banks’ profitability in Bangladesh.

Governments enforce banking regulations to achieve a stable and efficient financial system [ 20 , 94 ]. The existing literature is inconclusive on the effects of regulatory compliance on banks’ risks or the reduction of NPAs [ 10 , 11 ]. Boudriga et al. [ 25 ] concluded that the regulatory mechanism plays an insignificant role in reducing NPAs. This is especially true in weak institutions, which are susceptible to corruption. Gonzalez [ 52 ] reported that firm regulations have a positive relationship with banks’ risk-taking, increasing the probability of NPAs. However, Boudriga et al. [ 25 ], Samitas and Polyzos [ 113 ], and Allen et al. [ 3 ] strongly oppose the use of regulation as a tool to reduce banks’ risk-taking.

Kwan and Laderman [ 79 ] proposed three levels in regulating banks, which are lax, liberal, and strict. The liberal regulatory framework leads to more diversification in banks. By contrast, the strict regulatory framework forces the banks to take inappropriate risks to compensate for the loss of business; this is a global problem [ 73 ].

Capital regulation reduces banks’ risk-taking [ 103 , 110 ]. Capital regulation leads to cost escalation, but the benefits outweigh the cost [ 103 ]. The trade-off is worth striking. Altman Z score is used to predict banks’ bankruptcy, and it found that the regulation increased the Altman’s Z-score [ 4 , 46 , 63 , 68 , 72 , 120 ]. Jin et al. [ 62 ] report a negative relationship between regulation and banks’ risk-taking. Capital requirements empowered regulators, and competition significantly reduced banks’ risk-taking [ 1 , 122 ]. Capital regulation has a limited impact on banks’ risk-taking [ 90 , 103 ].

Maji and De [ 90 ] suggested that human capital is more effective in managing banks’ credit risks. Besanko and Kanatas [ 21 ] highlighted that regulation on capital requirements might not mitigate risks in all scenarios, especially when recapitalization has been enforced. Klomp and De Haan [ 72 ] proposed that capital requirements and supervision substantially reduce banks’ risks.

A third-party audit may impart more legitimacy to the banking system [ 23 ]. The absence of third-party intervention is conspicuous, and this may raise a doubt about the reliability and effectiveness of the impact of regulation on bank’s risk-taking.

NPA (risk-taking) in banks and profitability

Profitability affects NPAs, and NPAs, in turn, affect profitability. According to the bad management hypothesis [ 17 ], higher profits would negatively affect NPAs. By contrast, higher profits may lead management to resort to a liberal credit policy (high earnings), which may eventually lead to higher NPAs [ 104 ].

Balasubramaniam [ 8 ] demonstrated that NPA has double negative effects on banks. NPAs increase stressed assets, reducing banks’ productive assets [ 92 , 117 , 136 ]. This phenomenon is relatively underexplored and therefore renders itself for future research.

Triad and the performance of banks

Regulation and triad.

Regulations and their impact on banks have been a matter of debate for a long time. Barth et al. [ 12 ] demonstrated that countries with a central bank as the sole regulatory body are prone to high NPAs. Although countries with multiple regulatory bodies have high liquidity risks, they have low capital requirements [ 40 ]. Barth et al. [ 12 ] supported the following steps to rationalize the existing regulatory mechanism on banks: (1) mandatory information [ 22 ], (2) empowered management of banks, and (3) increased incentive for private agents to exert corporate control. They show that profitability has an inverse relationship with banks’ risk-taking [ 114 ]. Therefore, standard regulatory practices, such as capital requirements, are not beneficial. However, small domestic banks benefit from capital restrictions.

DeYoung and Jang [ 43 ] showed that Basel III-based policies of liquidity convergence ratio (LCR) and net stable funding ratio (NSFR) are not fully executed across the globe, including the US. Dahir et al. [ 39 ] found that a decrease in liquidity and funding increases banks’ risk-taking, making banks vulnerable and reducing stability. Therefore, any regulation on liquidity risk is more likely to create problems for banks.

Concentration banking and triad

Kiran and Jones [ 71 ] asserted that large banks are marginally affected by NPAs, whereas small banks are significantly affected by high NPAs. They added a new dimension to NPAs and their impact on profitability: concentration banking or banks’ market power. Market power leads to less cost and more profitability, which can easily counter the adverse impact of NPAs on profitability [ 6 , 15 ].

The connection between the huge volume of research on the performance of banks and competition is the underlying concept of market power. Competition reduces market power, whereas concentration banking increases market power [ 25 ]. Concentration banking reduces competition, increases market power, rationalizes the banks’ risk-taking, and ensures profitability.

Tabak et al. [ 125 ] advocated that market power incentivizes banks to become risk-averse, leading to lower costs and high profits. They explained that an increase in market power reduces the risk-taking requirement of banks. Reducing banks’ risks due to market power significantly increases when capital regulation is executed objectively. Ariss [ 6 ] suggested that increased market power decreases competition, and thus, NPAs reduce, leading to increased banks’ stability.

Competition, the performance of banks, and triad

Boyd and De Nicolo [ 27 ] supported that competition and concentration banking are inversely related, whereas competition increases risk, and concentration banking decreases risk. A mere shift toward concentration banking can lead to risk rationalization. This finding has significant policy implications. Risk reduction can also be achieved through stringent regulations. Bolt and Tieman [ 24 ] explained that stringent regulation coupled with intense competition does more harm than good, especially concerning banks’ risk-taking.

Market deregulation, as well as intensifying competition, would reduce the market power of large banks. Thus, the entire banking system might take inappropriate and irrational risks [ 112 ]. Maji and Hazarika [ 91 ] added more confusion to the existing policy by proposing that, often, there is no relationship between capital regulation and banks’ risk-taking. However, some cases have reported a positive relationship. This implies that banks’ risk-taking is neutral to regulation or leads to increased risk. Furthermore, Maji and Hazarika [ 91 ] revealed that competition reduces banks’ risk-taking, contrary to popular belief.

Claessens and Laeven [ 36 ] posited that concentration banking influences competition. However, this competition exists only within the restricted circle of banks, which are part of concentration banking. Kasman and Kasman [ 66 ] found that low concentration banking increases banks’ stability. However, they were silent on the impact of low concentration banking on banks’ risk-taking. Baselga-Pascual et al. [ 14 ] endorsed the earlier findings that concentration banking reduces banks’ risk-taking.

Concentration banking and competition are inversely related because of the inherent design of concentration banking. Market power increases when only a few large banks are operating; thus, reduced competition is an obvious outcome. Barra and Zotti [ 9 ] supported the idea that market power, coupled with competition between the given players, injects financial stability into banks. Market power and concentration banking affect each other. Therefore, concentration banking with a moderate level of regulation, instead of indiscriminate regulation, would serve the purpose better. Baselga-Pascual et al. [ 14 ] also showed that concentration banking addresses banks’ risk-taking.

Schaeck et al. [ 115 ], in a landmark study, presented that concentration banking and competition reduce banks’ risk-taking. However, they did not address the relationship between concentration banking and competition, which are usually inversely related. This could be a subject for future research. Research on the relationship between concentration banking and competition is scant, identified as a research gap (“ Research Implications of the study ” section).

Transparency, corporate governance, and triad

One of the big problems with NPAs is the lack of transparency in both the regulatory bodies and banks [ 25 ]. Boudriga et al. [ 25 ] preferred to view NPAs as a governance issue and thus, recommended viewing it from a governance perspective. Ahmad and Ariff [ 2 ] concluded that regulatory capital and top-management quality determine banks’ credit risk. Furthermore, they asserted that credit risk in emerging economies is higher than that of developed economies.

Bad management practices and moral vulnerabilities are the key determinants of insolvency risks of Indian banks [ 95 ]. Banks are an integral part of the economy and engines of social growth. Therefore, banks enjoy liberal insolvency protection in India, especially public sector banks, which is a critical issue. Such a benevolent insolvency cover encourages a bank to be indifferent to its capital requirements. This indifference takes its toll on insolvency risk and profit efficiency. Insolvency protection makes the bank operationally inefficient and complacent.

Foreign equity and corporate governance practices help manage the adverse impact of banks’ risk-taking to ensure the profitability and stability of banks [ 33 , 34 ]. Eastburn and Sharland [ 45 ] advocated that sound management and a risk management system that can anticipate any impending risk are essential. A pragmatic risk mechanism should replace the existing conceptual risk management system.

Lo [ 87 ] found and advocated that the existing legislation and regulations are outdated. He insisted on a new perspective and asserted that giving equal importance to behavioral aspects and the rational expectations of customers of banks is vital. Buston [ 29 ] critiqued the balance sheet risk management practices prevailing globally. He proposed active risk management practices that provided risk protection measures to contain banks’ liquidity and solvency risks.

Klomp and De Haan [ 72 ] championed the cause of giving more autonomy to central banks of countries to provide stability in the banking system. Louzis et al. [ 88 ] showed that macroeconomic variables and the quality of bank management determine banks’ level of NPAs. Regulatory authorities are striving hard to make regulatory frameworks more structured and stringent. However, the recent increase in loan defaults (NPAs), scams, frauds, and cyber-attacks raise concerns about the effectiveness [ 19 ] of the existing banking regulations in India as well as globally.

Discussion of the findings

The findings of this study are based on the bibliometric and content analysis of the sample published articles.

The bibliometric study concludes that there is a growing demand for researchers and good quality research

The keyword analysis suggests that risk regulation, competition, profitability, and performance are key elements in understanding the banking system. The main authors, keywords, and journals are grouped in a Sankey diagram in Fig.  6 . Researchers can use the following information to understand the publication pattern on banking and its determinants.

Sankey Diagram of main authors, keywords, and journals. Note Authors contribution using scientometrics tools

Research Implications of the study

The study also concludes that a balance among the three components of triad is the solution to the challenges of banks worldwide, including India. We propose the following recommendations and implications for banks:

This study found that “the lesser the better,” that is, less regulation enhances the performance and risk management of banks. However, less regulation does not imply the absence of regulation. Less regulation means the following:

Flexible but full enforcement of the regulations

Customization, instead of a one-size-fits-all regulatory system rooted in a nation’s indigenous requirements, is needed. Basel or generic regulation can never achieve what a customized compliance system can.

A third-party audit, which is above the country's central bank, should be mandatory, and this would ensure that all three aspects of audit (policy formulation, execution, and audit) are handled by different entities.

Competition

This study asserts that the existing literature is replete with poor performance and risk management due to excessive competition. Banking is an industry of a different genre, and it would be unfair to compare it with the fast-moving consumer goods (FMCG) or telecommunication industry, where competition injects efficiency into the system, leading to customer empowerment and satisfaction. By contrast, competition is a deterrent to the basic tenets of safe banking. Concentration banking is more effective in handling the multi-pronged balance between the elements of the triad. Concentration banking reduces competition to lower and manageable levels, reduces banks’ risk-taking, and enhances profitability.

No incentive to take risks

It is found that unless banks’ risk-taking is discouraged, the problem of high NPA (risk-taking) cannot be addressed. Concentration banking is a disincentive to risk-taking and can be a game-changer in handling banks’ performance and risk management.

Research on the risk and performance of banks reveals that the existing regulatory and policy arrangement is not a sustainable proposition, especially for a country where half of the people are unbanked [ 37 ]. Further, the triad presented by Keeley [ 67 ] is a formidable real challenge to bankers. The balance among profitability, risk-taking, and regulation is very subtle and becomes harder to strike, just as the banks globally have tried hard to achieve it. A pragmatic intervention is needed; hence, this study proposes a change in the banking structure by having two types of banks functioning simultaneously to solve the problems of risk and performance of banks. The proposed two-tier banking system explained in Fig.  7 can be a great solution. This arrangement will help achieve the much-needed balance among the elements of triad as presented by Keeley [ 67 ].

Conceptual Framework. Note Fig.  7 describes the conceptual framework of the study

The first set of banks could be conventional in terms of their structure and should primarily be large-sized. The number of such banks should be moderate. There is a logic in having only a few such banks to restrict competition; thus, reasonable market power could be assigned to them [ 55 ]. However, a reduction in competition cannot be over-assumed, and banks cannot become complacent. As customary, lending would be the main source of revenue and income for these banks (fund based activities) [ 82 ]. The proposed two-tier system can be successful only when regulation especially for risk is objectively executed [ 29 ]. The second set of banks could be smaller in size and more in number. Since they are more in number, they would encounter intense competition for survival and for generating more business. Small is beautiful, and thus, this set of banks would be more agile and adaptable and consequently more efficient and profitable. The main source of revenue for this set of banks would not be loans and advances. However, non-funding and non-interest-bearing activities would be the major revenue source. Unlike their traditional and large-sized counterparts, since these banks are smaller in size, they are less likely to face risk-taking and NPAs [ 74 ].

Sarmiento and Galán [ 114 ] presented the concerns of large and small banks and their relative ability and appetite for risk-taking. High risk could threaten the existence of small-sized banks; thus, they need robust risk shielding. Small size makes them prone to failure, and they cannot convert their risk into profitability. However, large banks benefit from their size and are thus less vulnerable and can convert risk into profitable opportunities.

India has experimented with this Differential Banking System (DBS) (two-tier system) only at the policy planning level. The execution is impending, and it highly depends on the political will, which does not appear to be strong now. The current agenda behind the DBS model is not to ensure the long-term sustainability of banks. However, it is currently being directed to support the agenda of financial inclusion by extending the formal credit system to the unbanked masses [ 107 ]. A shift in goal is needed to employ the DBS as a strategic decision, but not merely a tool for financial inclusion. Thus, the proposed two-tier banking system (DBS) can solve the issue of profitability through proper regulation and less risk-taking.

The findings of Triki et al. [ 130 ] support the proposed DBS model, in this study. Triki et al. [ 130 ] advocated that different component of regulations affect banks based on their size, risk-taking, and concentration banking (or market power). Large size, more concentration banking with high market power, and high risk-taking coupled with stringent regulation make the most efficient banks in African countries. Sharifi et al. [ 119 ] confirmed that size advantage offers better risk management to large banks than small banks. The banks should modify and work according to the economic environment in the country [ 69 ], and therefore, the proposed model could help in solving the current economic problems.

This is a fact that DBS is running across the world, including in India [ 60 ] and other countries [ 133 ]. India experimented with DBS in the form of not only regional rural banks (RRBs) but payments banks [ 109 ] and small finance banks as well [ 61 ]. However, the purpose of all the existing DBS models, whether RRBs [ 60 ], payment banks, or small finance banks, is financial inclusion, not bank performance and risk management. Hence, they are unable to sustain and are failing because their model is only social instead of a much-needed dual business-cum-social model. The two-tier model of DBS proposed in the current paper can help serve the dual purpose. It may not only be able to ensure bank performance and risk management but also serve the purpose of inclusive growth of the economy.

Conclusion of the study

The study’s conclusions have some significant ramifications. This study can assist researchers in determining their study plan on the current topic by using a scientific approach. Citation analysis has aided in the objective identification of essential papers and scholars. More collaboration between authors from various countries/universities may help countries/universities better understand risk regulation, competition, profitability, and performance, which are critical elements in understanding the banking system. The regulatory mechanism in place prior to 2008 failed to address the risk associated with banks [ 47 , 87 ]. There arises a necessity and motivates authors to investigate the current topic. The present study systematically explores the existing literature on banks’ triad: performance, regulation, and risk management and proposes a probable solution.

To conclude the bibliometric results obtained from the current study, from the number of articles published from 1976 to 2020, it is evident that most of the articles were published from the year 2010, and the highest number of articles were published in the last five years, i.e., is from 2015. The authors discovered that researchers evaluate articles based on the scope of critical journals within the subject area based on the detailed review. Most risk, regulation, and profitability articles are published in peer-reviewed journals like; “Journal of Banking and Finance,” “Journal of Accounting and Economics,” and “Journal of Financial Economics.” The rest of the journals are presented in Table 1 . From the affiliation statistics, it is clear that most of the research conducted was affiliated with developed countries such as Malaysia, the USA, and the UK. The researchers perform content analysis and Citation analysis to access the type of content where the research on the current field of knowledge is focused, and citation analysis helps the academicians understand the highest cited articles that have more impact in the current research area.

Practical implications of the study

The current study is unique in that it is the first to systematically evaluate the publication pattern in banking using a combination of scientometrics analysis tools, network analysis tools, and content analysis to understand the relationship between bank regulation, performance, and risk. The study’s practical implications are that analyzing existing literature helps researchers generate new themes and ideas to justify their contribution to literature. Evidence-based research knowledge also improves decision-making, resulting in better practical implementation in the real corporate world [ 100 , 129 ].

Limitations and scope for future research

The current study only considers a single database Scopus to conduct the study, and this is one of the limitations of the study spanning around the multiple databases can provide diverse results. The proposed DBS model is a conceptual framework that requires empirical testing, which is a limitation of this study. As a result, empirical testing of the proposed DBS model could be a future research topic.

Availability of data and materials

SCOPUS database.

Abbreviations

Systematic literature review

World Financial Crisis

Non-performing assets

Differential banking system

SCImago Journal Rank Indicator

Liquidity convergence ratio

Net stable funding ratio

Fast moving consumer goods

Regional rural banks

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Rastogi, S., Sharma, A., Pinto, G. et al. A literature review of risk, regulation, and profitability of banks using a scientometric study. Futur Bus J 8 , 28 (2022). https://doi.org/10.1186/s43093-022-00146-4

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Banking Credit Risk Management Essay

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Credit Risk Models

The black-scholes-merton model.

There are various credit risk models, which have been proposed by researchers in order to reduce the risks associated with future transactions. One of these models is the BSM framework structured models. This model was proposed by Merton in 1974. He derived the value of an option from a company, which could default loan repayment (Merton, 1974).

The Black-Scholes-Merton model assumes that there is a latent firm asset value, which is determined by the company’s future cash flows.

This model is used to determine the firm’s debt and equity value. This model includes components, such as risk free interest rates, asset payment ratio, and asset risk premium. Merton (1974) argues that the asset return rate and the risk free interest rates are the constants which are non-stochastic.

The model also assumes that the company’s capital structure relates to a pure equity and a single zero coupon debt, which matures within a given time. Incase of a default experienced by a company, the stock price of the defaulting firm is expected to go to zero. According to this model, the debtor is assumed to be a seller in the European put option.

On the other hand, the equity holder is assumed to be a buyer of European call option (Merton, 1974). The model uses the Black –Scholes option pricing in order to determine the relationship between the equity market value and the bond market value. Shibita and Yamada (2009) proposed the BSM structural model to a bank, which was just this side of bankruptcy.

This helped the bank in its recovery processes. According to Shibata and Yamada (2009), the bank’s choice to continue operating or be liquidated plays a vital role on the losses of the loan. They assumed that this decision should be made severally at a certain time after the bankruptcy.

Individual-level reduced-form models

This refers to the models which are not of the class of structural models. At the individual level, a reduced form model can also be described as the credit scoring one. This model was developed by Altman (1968). The credit scoring model uses linear and binomial models to regress the defaults among companies.

It identifies various accounting components, which have statistical explanatory capability, in order to differentiate between the defaulting and non defaulting firms. After estimating the coefficients of the model, the loan applicants are given a Z-score in order to classify them as good or bad. Several decades after its proposal, the credit scoring model got a significant development.

Altaman and Saunders (1998) discussed the wide spread of the individual level model and its major developments over the years. Altman and Narayanan (1997) evaluated the historical accounting variables used in the credit scoring models across the world.

According to them, most of the studies proposed the use of financial ratios, which determine profitability, and liquidity. These financial ratios may include market value equity/debt, (EBIT)/sales as well as working capital/debt. Altman (2005) proposed a scoring system known as Emerging Market Score Model to use to define the emerging corporate bonds.

Portfolio reduced form models

These models were proposed by Jarrow and Turnbull (1992). According to them, the idea of these models is related to the concept of risk neutral. Risk neutral is a common technique used to predict the probability of the future cash flow.

It helps in computing the asset prices by using risk neutral default probabilities. Jarrow and Turnbull (1992) used the idea of risk neutral to develop the credit risk premium which is also known as the credit spread. They decomposed the credit risk premium into two components.

Poisson /Cox process model

This is a subclass of the portfolio reduced form models. It was developed by Jarrow and Turnbull (1995), and it can be described as the simplest model of the portfolio reduced form model. In this approach, the default process is assumed to be a Poisson process with a constant intensity where the default time is exponentially distributed.

Markov chain model

This is a credit risk model, which was originally proposed by Jarrow et al. (1997). This model considers the default event as the absorbing state and the default period as the first period when the Markov chain hits the absorbing state.

Factor model

This is a credit risk model, which puts into consideration two vectors of explanation variables. The first vector is a set of macro economic variables, such as interest rate, inflation rate, money supply growth as well as GDP growth. This vector explains the systematic risk, which causes default events.

The second vector involves a set of firm-specific variables, which determine individual risk. According to Pederzoli and Torricelli (2005), the variables are considered simultaneously.

The credit risk models have various shortcomings. For instance, the BSM framework structural model consists of several simplified assumption in its derivation. The simplified assumptions restrict the applied value of the model. This has made the subsequent researchers focus on reducing these assumptions.

The individual level reduced form models may not pick up fast moving developments in borrower’s conditions. This is because the model uses explanatory variables, which are based on accounting data. According to Agarwal and Taffler’s (2008), credit scoring models, such as Altman’s Z-score, may not be used to forecast distress as compared to the structural models.

Altman, E 2005, ‘An emerging market credit scoring system for corporate bonds’, Journal of Emerging Markets Review , vol. 6, no. 4, pp. 311-323.

Altman, E, & Saunders, A 1998, ‘Credit risk measurement : Developments over the last 20 years’, Journal of Banking and Finance , vol. 21, pp. 1721-1742.

Gordy, MB, 2000, ‘A comparative anatomy of credit risk models’, Journal of Banking and Finance , pp.119-149.

Jarrow, R, & Turnbull, S 1997 ‘A Markov model for the term structure of credit risk spreads’, Review of Financial Studies, vol. 10, no. 2, pp. 481–523.

Merton, C 1974. On the pricing of corporate debt: The risk structure of interest rates. The Journal of Finance , vol. 29, no. 2, pp. 449–470.

Saunders, A & Allen, L 2002, Credit risk measurement: New Approaches to Value at Risk and Other Paradigms . John Wiley & Sons, New York.

• Interest Rate Disparity between RBA and Major Australian Banks
• Big Four: Banking in China
• Definition of the Credit Ratings
• Fannie Mae, Freddie Mac, and the Credit Crunch
• Altman Corporation's Dress Code Policy Memorandum
• Money and Capital Markets: Turkey, India and China
• Money and Capital Markets: Central Banks
• Market Elasticity’s in Banking Industry
• Purchasing Power Parity Definition and Challenges
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Banking Risk Management and Performance

Introduction

The various global financial downturns experienced in the past have underlined the relevance of engaging in effective risk management to enhance the sustainability of different industries. The banking sector is one of the industries faced with numerous risks that could undermine the performance of the financial institutions considerably. As such, there is continued emphasis for the banking players to engage in risk management practices in their daily operations for the sake of creating long-term value as well as being in line with the current regulations on threat mitigation (O’Kelly 69).

Importantly, the latest entrants in the banking industry need to consider the various risks associated with aspects such as the fluctuation of currency prices, credit, interest, liquidity, operations, market, and reputation to bolster their survival in the new environment (Kanchu and Kumar 147). In this regard, Bank A, which seeks to extend its operations beyond Mainland China by establishing a branch in Hong Kong, requires considering and understanding the various types of risks they could encounter in the new environment. Therefore, this paper provides advice to Mr. Chen, the chief executive of Bank A, regarding the various types of risks the bank could face while operating in Hong Kong as well as the ways of managing some of the threats.

The Major Types of Risks Faced by Banks in their Daily Operations

There are several types of risks encountered by banks. The common threats concern the elements of the market, credit, liquidity, operations, and reputation. Bank A is likely to experience most of these risks, and thus understanding them is crucial before integrating the fundamental threat management approaches in its daily operations.

Market Risks

The banking market subjects the players in the industry to an array of risks. Interest rate risks, currency risks, and price risks influence the marker in the banking sector. The risks constitute the external forces that affect the performance of a bank either positively or negatively.

Interest Rate Risk

The dynamic nature of the interest rates in the banking market could have an adverse impact on a bank. Essentially, the time differences in maturity, as well as changes in prices of liabilities, assets, and off-balance sheet commitments, have significant effects. A financial institution needs to manage its interest risks by monitoring the repricing gap of the bank’s liabilities and assets carefully (O’Kelly 124). The strategy is crucial for safeguarding the bank’s interest, and thus operating profitably. Besides the repricing factor, the basis risk is another source of interest rate risk that could influence the operations of Bank A. The basis risk emanates from the variance in the differences in the interest rates gained and paid on various financial instruments bearing similar repricing attributes (Kanchu and Kumar 149).

Currency Risk

The currency risk emanates from the continually changing foreign exchange rates. Some daily engagements that could expose a bank to currency risks include commercial banking operations, foreign exchange deals, and the currency structure (Hull 106). Therefore, Bank A needs to manage its currency risks within acceptable limits to avert the adversities of money fluctuations. The strength of the currency used to handle the liabilities and assets of the bank is crucial for minimizing the associated risks resulting from foreign exchange variations. Therefore, it is advisable for Bank A to denominate its assets and liabilities in United States dollars and Hong Kong dollars among other stable currencies. The foreign exchange market has a considerable influence on the operations of a bank that serves various customers. Therefore, it is necessary for risk managers to regularly take note of the foreign currency trends (Christoffersen 129).

The price risk affects the capital and earning of a bank significantly as a result of alterations in the prices of securities. The different prices of securities such as equities, bonds, and commodities influence the level of earning as well as the capital possessed by an operational bank (Kanchu and Kumar 146). For instance, changes in the price of stocks triggering equity risk could expose Bank A to potential losses that can lead to the failure of the new entrant into the Honk Kong financial market. Further, the dynamic aspect of commodity prices owing to the current demand and supply patterns also poses a risk to the profitability of a banking sector player. The prices adopted for different transactions and open positions for facilitating the daily operations of a financial institution have a considerable influence on profitability.

Credit Risk

The credit risk arises from the failure of a bank borrower or counterparty to settle the payment of a credit facility as per the agreements with the bank (O’Kelly 98). The defaulters usually expose the bank to financial constraints that undermine the efficiency of their operations. The uncertainty entailed in the repayment of the credit offerings and the time aspects of repaying the dues expose the bank to credit risk. It is important to note that all banks in the financial industry face credit risk in their daily operations, and Bank A is not an exception. Factors including business failure and income inadequacy influence borrowers to default credit agreement with a bank. Notably, credit risk affects the bank negatively by declining the value of credit assets besides lowering the attainable profits from credit transactions (Duffie and Singleton 34).

Liquidity Risk

Liquidity risk exposes a bank to a situation where it fails to meet the costs of its day-to-day operations (Christoffersen140). Normally, a bank should have streamlined liquidity, which enables it to fulfill payment requirements from its primary operations as well as accumulate sufficient money to offer loans. The liquidity risk could predispose Bank A to a bank run where account holders might consider withdrawing their money from the bank, thereby escalating the financial position of the institution. In an adverse situation, a bank affected by the liquidity risk may be put under receivership where the government intervenes to protect it from total collapse. Therefore, Bank A needs to manage liquidity risk effectively to avoid tarnishing its image in the Hong Kong banking industry.

Operational Risk

Operational risks refer to the loss experienced by a bank due to the inefficiencies of internal processes, human resources, and systems as well as external events (Christoffersen145). In most cases, human errors and mistakes trigger operational risks. Hence, it is imperative for professionals to be keen in observing the conventional processes required to execute different tasks. The human risk aspect of operational risks could either be triggered willingly or unconsciously. An example of an operational risk that Bank A might face in the Honk Kong environment is the erroneous filing of information when clearing checks. The leaking of confidential information is another example of an error that could expose the bank to operational risks. The adopted computer systems could also subject a bank to operational risks if such systems fail or undergo programming errors. Since contemporary banks consider the integration of technology systems as crucial, it is important for a bank to ensure that its systems work efficiently to minimize the occurrence of operational risks (Bushman and Williams 4).

Reputational Risk

Reputational risks arise from the engagement in activities that can damage a bank’s brand image, thereby affecting the earnings, capital, or liquidity of the financial institution negatively (Kanchu and Kumar 152). Failure to observe the values and beliefs of the bank may prompt it to engage in inappropriate undertakings that have the potential of tarnishing the image of its brand significantly. Further, unethical practices within the bank can damage the reputation of the institution, thus undermining its competitiveness in the industry. Other triggers of reputational risk include failure to comply with regulations, poor customer service, misleading rumors about the bank, and decisions taken by the bank in critical circumstances (Hull 114). In this light, Bank A should be aware that every action it takes is judged by the stakeholders including customers, investors, and opinion leaders.

The Management of Credit Risk and Operational Risk

An international bank needs to manage risks effectively to boost its competitiveness in the industry. Credit risk and business risk constitute the two most important risks that call for the adoption of practices, which undermine their occurrence. In the case of Bank A, efficient management of the credit and operational risks is crucial for bolstering profitable operations in Hong Kong.

Managing Credit Risk

The management of credit risk requires the establishment of a suitable environment that reduces the chances of a bank incurring losses after offering financial facilities to customers (Hull 120). In this case, the bank needs to know the customers before transacting with them as it is one of the key steps that facilitate the success of the credit process. Thus, the acquisition of pertinent, accurate, and timely information about the client is crucial for creating an environment that reduces the possibility of offering customers credit facilities blindly.

The bank needs to apply a streamlined credit-granting process. In this respect, it should analyze both the financial and non-financial risks before granting the customer a credit facility (Duffie and Singleton 35). The bank needs to use different strategies for identifying risk, which include identification, analysis, quantification, mitigation, monitoring, and anticipation. Doing this before granting the credit package to the consumer minimizes the associated risks.

The maintenance of a proper credit administration, measurement, and monitoring approach are also crucial for an international bank to effectively manage financial risks (Bushman and Williams 7). The credit management plan should consider aspects such as the projection of the individual or business performance in the future, the anticipation of challenges, matching the appropriate loan, agreement development, and securing the credit through collateral and guarantors (Duffie and Singleton 37). The measurement element should oblige a bank to price the deal in a way that the credit repayment would compensate the transaction adequately. Monitoring the relationship requires the financial institutions to assess the financial profile of the customer regularly.

The efficient management of credit risk necessitates the financial system to control a transaction. Controlling credit risk facilitates proper tracking of the threat, thus reducing the element of surprise (Hull 81). The provisions of the deal need to ensure that the bank has total control of the terms and conditions that secure the interest of both the financial institution and the borrower.

Managing Operational Risk

A financial institution needs to explain operational risk clearly to the staff to foster an understanding of the issue and how it impacts their performance (Bushman and Williams 12). Thus, a functional risk policy needs to be implemented across all the business lines in the financial institution. The approach is essential since it facilitates the identification of the causes of operational risk.

The creation of a culture that promotes operational risk awareness is necessary for a financial institution. A bank should conduct a regular review of the behaviors of the staff to reduce errors that might expose the organization to operational risks (Bushman and Williams 8). Constant communication of the acceptable code of conduct and working standards is also critical in eliminating operational risks in a bank.

The management should oversee the processes undertaken by the junior employees of a financial institution to ensure that they engage in practices, which are in line with the acceptable standards (Hull 77). The involvement of operation managers among other senior officials in supervising employees in a bank shows their commitment to ensuring that the right procedures are followed when providing different financial services to customers.

Financial institutions are exposed to an array of risks that require consideration and management to foster competitiveness. The main types of risks in the banking sector include credit risk, operational risk, market risk, liquidity risk, and reputational risk. The adoption of practices that identify and mitigate the risks is crucial for facilitating the efficient running of a financial institution.

Works Cited

Bushman, Robert, and Christopher Williams. “Accounting Discretion, Loan Loss Provisioning, and Discipline of Banks’ Risk-Taking.” Journal of Accounting and Economics , vol. 54, no. 1, 2012, pp. 1-18.

Christoffersen, Peter. Elements of Financial Risk Management . Academic Press, 2012.

Duffie, Darrell, and Kenneth Singleton. Credit Risk: Pricing, Measurement, and Management . Princeton University Press, 2012.

Hull, John. Risk Management and Financial Institutions, + Web Site . John Wiley & Sons, 2012.

Kanchu, Thirupathi, and Manoj Kumar. “Risk Management in Banking Sector – An Empirical Study.” International Journal of Marketing, Financial Services & Management Research , vol. 2, no. 2, 2013, pp. 145-153.

O’Kelly, Brian. Risk Management in Banking . John Wiley & Sons, 2015.

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Overconfident Bank CEOS: Risk Amplification Amid Economic Policy Uncertainty?

48 Pages Posted: 3 Sep 2024

Kwabena A. Addo

Utrecht University

Shams Pathan

University of Newcastle - Newcastle University Business School

Steven Ongena

University of Zurich - Department Finance; Swiss Finance Institute; KU Leuven; NTNU Business School; Centre for Economic Policy Research (CEPR)

Overconfident Bank CEOs: Risk Amplification Amid Economic Policy Uncertainty?

We examine whether overconfident bank CEOs mitigate or amplify risk amid increasing Economic Policy Uncertainty (EPU). Our findings indicate the latter, with banks led by overconfident CEOs then assuming almost 2% more risk on average than other banks. We also find that this effect does not depend on the CEO’s gender or the political inclination of the state where the bank is headquartered. Overconfident CEOs contribute to high loan impairments during high EPU periods through excessive credit extension and under-provision of loan reserves, propagating a cycle of risky credit extension, which enhances overall bank performance, however. Investors, board members, and regulators should therefore be mindful of the systemic implications of their policies.

Keywords: Economic Policy Uncertainty, CEO overconfidence, banking risk, bank lending, banking performance.

Suggested Citation: Suggested Citation

Utrecht University ( email )

Vredenburg 138 Utrecht, 3511 BG Netherlands

University of Newcastle - Newcastle University Business School ( email )

5 Barrack Road Frederic Douglass Centre NEWCASTLE UPON TYNE, NE4 5TG United Kingdom

Steven R. G. Ongena (Contact Author)

University of zurich - department finance ( email ).

Schönberggasse 1 Zürich, 8001 Switzerland

Swiss Finance Institute

c/o University of Geneva 40, Bd du Pont-d'Arve CH-1211 Geneva 4 Switzerland

KU Leuven ( email )

Oude Markt 13 Leuven, Vlaams-Brabant 3000 Belgium

NTNU Business School ( email )

Centre for economic policy research (cepr).

London United Kingdom

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World Bank Supports Moldova to Boost Preparedness for Natural Disasters, Climate Shocks

CHISINAU, September 5, 2024 —The World Bank’s Board of Executive Directors approved today a $40 million financing package for the Strengthening Moldova’s Disaster Risk Management and Resilience (SMORE) Project , which aims to bolster Moldova’s preparedness for and response to natural hazards and climate-related shocks that threaten lives, homes, and critical infrastructure.

The SMORE Project takes a comprehensive approach to improving disaster risk management in Moldova through three components:

• Installing and implementing a national cell broadcast-based public warning system: This system, integrated with existing meteorological, hydrological, and geological information systems, will significantly enhance the country’s preparedness against natural hazards like floods, fires, and earthquakes. The project also supports procuring emergency response vehicles, such as fire engines, and specialized equipment for search, rescue, and logistics operations, enhancing national and local-level emergency response services.
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• Aligning the country’s seismic design and retrofitting standards with EU building codes, particularly for earthquake resilience: This involves developing national methodologies for seismic and disaster risk mapping and rapid visual screening of buildings for seismic vulnerability. The project will also help redesign and enhance the country’s existing disaster reserve fund to provide transparent, rule-based, and targeted funding in response to hazards that are expected to become more intense and frequent due to climate change.

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The SMORE Project is supported through grant funds provided by the Global Facility for Disaster Reduction and Recovery (GFDRR) and the Technical Assistance Financing Facility (TAFF) for Disaster Prevention and Preparedness , which is financed by the European Commission’s Directorate-General for European Civil Protection and Humanitarian Aid Operations (DG ECHO) and administered by GFDRR.  

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Risk Analysis Specialist I

Job Description:

At Bank of America, we are guided by a common purpose to help make financial lives better through the power of every connection. Responsible Growth is how we run our company and how we deliver for our clients, teammates, communities and shareholders every day. One of the keys to driving Responsible Growth is being a great place to work for our teammates around the world. We’re devoted to being a diverse and inclusive workplace for everyone. We hire individuals with a broad range of backgrounds and experiences and invest heavily in our teammates and their families by offering competitive benefits to support their physical, emotional, and financial well-being. Bank of America believes both in the importance of working together and offering flexibility to our employees. We use a multi-faceted approach for flexibility, depending on the various roles in our organization. Working at Bank of America will give you a great career with opportunities to learn, grow and make an impact, along with the power to make a difference. Join us!

Overview of Global Risk Analytics

Bank of America has an opportunity for a Risk Analysis Specialist I within our Global Risk Analytics (GRA) function. GRA is a sub-line of business within Global Risk Management (GRM).  GRA is responsible for developing a consistent and coherent set of models and analytical tools for effective risk and capital measurement, management and reporting across Bank of America. GRA partners with the Lines of Business and Enterprise functions to ensure that its models and analytics address both internal and regulatory requirements, such as quarterly Enterprise Stress Testing (EST), the annual Comprehensive Capital Analysis and Review (CCAR), and the Current Expected Credit Losses (CECL) accounting standard.  GRA models follow an iterative and ongoing development life cycle, as the bank responds to the changing nature of portfolios, economic conditions and emerging risks.  In addition to model development, GRA conducts model implementation, data management, model execution and analysis, forecast administration, and model performance monitoring. GRA drives innovation, process improvement and automation across all of these activities. 

Overview of the Team

Global Markets Risk Analytics (GMRA) is part of Global Risk Analytics (GRA). It responsible for developing, maintaining, and monitoring counterparty credit risk and market risk models. GMRA also develops analytical tools to support regulatory, audit, and internal risk management needs for Global Markets. The role sits within the Prime Clearing Analytics and Data Services (PCADS) -  Data Controls & Integrity (DCI) team, and is responsible for ensuring adequate controls and processes exist to validate the firm’s traded products activity for the purposes of Credit and Capital calculations. The team is also heavily involved in process re-engineering, automation, and the development of our target state risk repository.

Overview of the Role

We are seeking an experienced Python Software Developer with basic understanding of systems in counterparty credit risk management within major banking institutions. The ideal candidate will play an important role within our compact development team and must possess extensive experience in enterprise software development. The individual will be responsible for contributing to the PCADS projects across all phases of the Software Development Life Cycle (SDLC), including analysis, design, development, unit testing, QA, UAT, and tier-2 production support.

• Design, development, and implementation of automated data integrity controls
• Writing reusable, testable, and efficient Python code.
• Work closely with lead developers to implement Python application architectures and designs.
• Maintain existing applications to support operations.
• Produce documents including design documents, and class diagrams.
• Perform testing with technical peers and functional end users to ensure successful launch of development projects.
• Support ad-hoc operational queries for DCI.
• Promote quality engineering and good coding practices across the company.

Required Education, Skills, and Experience

• BS in Computer Science, Financial Engineering, or a STEM field

• The candidate should possess a robust background in Python and Object Oriented Programming

• 2-5 years’ experience in the financial services industry (Large banks, brokers-dealers, hedge funds, or financial/data intermediary)

• Basic proficiency in MS Office suite is critical • Excellent verbal and written communication skills • Candidate must be able to manage time sensitive issues, prioritize multiple processes and projects, and articulate thought process for decision-making • Experience with Data Incident Tracking tools such as JIRA/eDIM

• The candidate must possess a personality that enables cooperative work with others

Desired Skills and Experience

• Proven programming skills (Python, Tableau, SQL, and object-oriented programming) • Prior experience working in Quartz development in Bank of America in a developer role is a plus

• Basic understanding of traded products in global markets, counterparty credit risk, collateral, netting, and exposures is beneficial

Job Description: Responsible for performing more complex analysis and is engaged in the development of modeling that maximizes profits and asset growth and minimizes credit and operating losses and other risk exposures. Provides analytical support on various product strategies to ensure company goals are met. Coordinates the production of performance reports senior management. Reviews and analyzes trends in current population distributions and recommends strategies. May participate in or develop more complex program models to extract data and use databases to provide statistical and financial modeling. Analyzes portfolio trends, concerning credit score cutoffs, loss trends, portfolio dynamics, and bureau scoring criteria. Will participate in the rollout of company-wide pilot programs developed as a result of programmed models. Duties primarily include the regular use of discretion and independent judgment. Programming experience and relevant degree or large data experience required.

• Attention to Detail
• Business Intelligence
• Data and Trend Analysis
• Risk Analytics
• Risk Management
• Adaptability
• Collaboration
• Continuous Improvement
• Data Visualization
• Project Management
• Business Acumen
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Hours Per Week:

Weekly Schedule:

Referral Bonus Amount:

Hours Per Week: 

Learn more about this role

JR-24027322

Manages People: No

Jersey City pay range:

$98,000 - $120,000 annualized salary, offers to be determined based on experience, education and skill set.

Discretionary incentive eligible

This role is eligible to participate in the annual discretionary plan. Employees are eligible for an annual discretionary award based on their overall individual performance results and behaviors, the performance and contributions of their line of business and/or group; and the overall success of the Company.

This role is currently benefits eligible . We provide industry-leading benefits, access to paid time off, resources and support to our employees so they can make a genuine impact and contribute to the sustainable growth of our business and the communities we serve.

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Credit Risk and Its Management | Essay | Banks | Risk Management

Here is an essay on ‘Credit Risk and Its Management’ for class 11 and 12. Find paragraphs, long and short essays on ‘Credit Risk and Its Management’ especially written for school and banking students. 

Credit Risk and Its Management

Essay Contents:

• Essay on Securitisation

Essay # 1. Concept of Credit Risk :

Conceptually credit risk is easily understandable. We all know that credit risk arises from lending activities of a bank. It arises when a borrower does not pay interest and/or installments as and when it falls due or in case where a loan is repayable on demand, the borrower fails to make the payment as and when demanded. Banks follow up for the payments and more often than not end up in receiving less than the amount that is due. The shortfall in payment is written off eventually to the debit of profit and loss account. This is the risk that arises from lending activities.

Credit Risk in banks not only arises in course of direct lending when funds are not repaid, it also arises in course of issuing guarantees or letters of credit when funds will not be forth coming upon crystallisation of the liability, or in the course of transactions involving treasury products when series of payments due from the counterparty cease or are not forthcoming, or in case of trading of securities if settlement is not effected or in case of cross-border exposure where free transfer of currency is restricted or ceases. The list is, however, not exhaustive.

Since, lending activities are usually spread across all the branches and controlling offices of banks, and lending activities typically command more than half of all risk taking activities of a bank, management of credit risk is very critical requirement of banks. In addition, communication of credit risk management policy of the bank across the entire organisation assumes importance as this risk taking activity is exercised across a large cross-section of branches and a planned approach is required to build a portfolio with desired characteristics.

Essay # 2. Management Framework of Credit Risk :

As in case of market risk management, credit risk management also involves finding answer to four key questions :

(a) What are the risks?

(b) Which, when and how much risk to accept that results in improving bottom-line?

(c) How can we monitor and control credit risk?

(d) Can we reduce the risk? And, if so then how?

Management processes are designed essentially to answer these questions.

Accordingly, credit risk management processes are sub-divided into following four parts:

1. Credit Risk Identification

2. Credit Risk Measurement

3. Credit Risk Monitoring and Control

4. Credit Risk Mitigation

Management of credit risk needs an organisation structure in place that can carry out the functions required for the purpose.

Essay # 3. Organisation Structure of Credit Risk :

Organisation for credit risk management is created with the objective of achieving compatibility in risk and business policies and to ensure their simultaneous implementation in a consistent manner. It involves setting risk limits based on objective measures of risk and simultaneously ensuring optimum risk adjusted return keeping in view capital constraint. It is a question of bank’s policy in balancing risks, returns and capital. Organisation for credit risk management should be able to achieve it.

Usually, Credit Risk Management organisation would consist of:

i. The Board of Directors

ii. The Risk Management Committee

iii. Credit Policy Committee (CPC)

iv. Credit Risk Management Department.

i. Board of Directors:

The Board of Directors has the overall responsibility for management of risks. The Board articulates credit risk management policies, procedures, aggregate risk limits, review mechanisms and reporting and auditing systems. The Board decides the level of credit risk for the bank as a whole, keeping in view its profit objective and capital planning.

ii. Risk Management Committee:

The Risk Management Committee is a Board level Sub-Committee.

The responsibilities of Risk Management Committee with regard to credit risk management aspects include the following:

i. Setting guidelines for credit risk management and reporting

ii. Ensure that credit risk management processes conform to the policy

iii. Setting up prudential limits and its periodical review

iv. Ensure robustness of measurement of risk models

v. Ensure proper manning for the processes

iii. Credit Policy Committee:

Credit Policy Committee, also called Credit Control Committee deals with issues relating to credit policy and procedures and to analyse, manage and control credit risk on a bank wide basis. The Committee formulates policies on standards for presentation of credit proposals, financial covenants, rating standards and benchmarks, delegation of credit approving powers, prudential limits on large credit exposures, asset concentrations, standards for loan collateral, portfolio management, loan review mechanism, risk concentrations, risk monitoring and evaluation, pricing of loans, provisioning, regulatory/legal compliance, etc.

iv. Credit Risk Management Department (CRMD):

Credit Risk Management Department (CRMD), which is independent of the Credit Administration Department, enforces and monitors compliance of the risk parameters and prudential limits set by the CPC. The CRMD also lays down risk assessment systems, monitor quality of loan portfolio, identify problems and correct deficiencies, develop MIS and undertake loan review/audit. The Department undertakes portfolio evaluations and conduct comprehensives studies on the environment to test the resilience of the loan portfolio.

People with necessary credit dispensation skills, abilities and knowledge are a critical input in the CRM process. Therefore CRM needs to evaluate the existing competency levels and identify the training needs to enhance the competency levels to meet the requirements for successful functioning of CRM on an ongoing basis.

Essay # 4. Risk Identification of Credit Risk :

Credit risk arises from potential changes in the credit quality of a borrower.

It has two components:

i. Default risk and

ii. Credit spread risk.

i. Default Risk :

Default risk is driven by the potential failure of a borrower to make promised payments, either partly or wholly. In the event of default, a fraction of the obligations will normally be paid. This is known as the recovery rate.

ii. Credit Spread Risk or Downgrade Risk :

If a borrower does not default, there is still risk due to worsening in credit quality. This results in the possible widening of the credit-spread. This is credit-spread risk. Usually this is reflected through rating downgrade. It is normally firm-specific.

Loans are not usually marked-to-market. Consequently, the only important factor is whether or not the loan is in default today (since this is the only credit event that can lead to an immediate loss). Capital market portfolios are marked-to-market. They have in addition credit spread volatility (continuous changes in the credit-spread). This is more likely to be driven by the market’s appetite for certain levels of risk. For example, the spreads on high-grade bonds may widen or tighten, although this need not necessarily be taken as an indication that they are more or less likely to default.

Default risk and downgrade risk are transaction level risks. Risks associated with credit portfolio as a whole is termed portfolio risk.

Portfolio risk has two components:

a. Systematic or Intrinsic Risk

b. Concentration Risk.

a. Systematic or Intrinsic Risk :

Portfolio risk is reduced due to diversification. If a portfolio is fully diversified, i.e. diversified across geographies, industries, borrowers, markets, etc., equitably, then the portfolio risk is reduced to a minimum level. This minimum level corresponds to the risks in the economy in which it is operating. This is systematic or intrinsic risk.

b. Concentration Risk :

If the portfolio is not diversified that is to say that it has higher weight in respect of a borrower or geography or industry etc., the portfolio gets concentration risk.

A portfolio is open to the systematic risk i.e., the risks associated with the economy. If economy as a whole does not perform well, the portfolio performance will be affected. That is why when an economy stagnates or faces negative or reduced growth, credit portfolio of banking industry as a whole shows indifferent performance. Credit portfolio having concentration in any segment would be affected if the segment does not perform well.

Measuring and managing credit risk, whether for loans, bonds or derivative securities, has become a key issue for financial institutions. The risk analysis can be performed either for stand-alone trades or for portfolios as a whole. The latter approach takes into account risk diversification across trades and borrowers. Ignoring risk diversification can lead to erroneous risk management decisions, increasing rather than reducing the risk exposure of the financial institution.

Diversification occurs both at the borrower (obligor) level and at geographical level besides across trades and industries. Since different firms do not default at the same time, the risk level and corresponding capital that must be held against defaults for a well-diversified portfolio is only a fraction of the total exposure of the portfolio.

The following chart outlines financial risks in lending as shown in Figure 12.1:

A variant of credit risk is ‘Counterparty Risk’. The counterparty risk arises from non-performance of the trading partners. The non-performance may arise from counterparty’s refusal/inability to perform. The counterparty risk is generally viewed as a transient financial risk associated with trading rather than standard credit risk.

‘Country Risk’ is also a type of credit risk where non-performance by a borrower or counterparty arises because of restrictions imposed by a sovereign. The restrictions may be in the nature of a sanction or may arise due to economic conditions.

Essay # 5. Measurement of Credit Risk :

Measurement of credit risk consists of:

(a) Measurement of risk through credit rating/scoring;

(b) Quantifying the risk through estimating expected loan losses, i.e., the amount of loan losses that bank would experience over a chosen time horizon (through tracking portfolio behaviour over 5 or more years) and unexpected loan losses i.e. the amount by which actual losses exceed the expected loss (through standard deviation of losses or the difference between expected loan losses and some selected target credit loss quartile).

i. Credit Rating – Why is it Necessary?

Credit Rating of an account is done with primary objective to determine whether the account, after the expiry of a given period, would remain a performing asset, i.e., it will continue to meet its obligation to its creditors, including Bank and would not be in default. In other words, credit rating exercise seeks to predict whether the borrower would have the capability to honour its financial commitment in future to the rest of the world.

There is no mathematical/econometric/empirical model, which can predict the future capability of a borrower to meet its financial obligations accurately. Nevertheless, lenders in financial market, all over the world, rely on some model, which seek to predict the future capability of a borrower to meet its financial obligations. This is because behaviour of a group of borrowers having similar rating, in terms of their failure to meet financial obligations i.e., having defaulted financially, has been found to be, within bounds, consistent.

This is explained below:

A borrower, even though he is rated ‘C’, may not default after a given period of say one year, whereas, another borrower rated ‘A’ may default at the end of the given period. This type of apparently paradoxical situation does arise and that too not infrequently. However, if 100 ‘C’ rated borrowers are tracked over a period of say one year and say 10 borrowers default in meeting their financial obligations, as against that, 100 ‘A’ rated borrowers, if tracked over the same period, only one borrower may default in meeting his financial obligation.

So lenders in financial market rate accounts to determine the class to which a borrower belongs. And based on the past record of default of the borrowers belonging to the same class, they have a fair estimate of possible number of defaults among the borrower belonging to the said class.

In other words, if a bank say has 200 borrowers who have been rated ‘A’ in their portfolio and if past record show that 1% of such borrowers have defaulted every year, they would estimate that 2 borrowers among these ‘A’ rated accounts may default at the end of a year. This in turn, helps the bank to assess cost of default to it, and is built into the loan pricing so that cost of default is recovered.

Similarly, if the bank has 400 borrowers who have been rated ‘B’ in their portfolio and if past records show that 5% of such borrowers have defaulted every year, they would estimate that 20 borrowers among these ‘B’ rated accounts might default at the end of a year. This then is to be built into the loan pricing for ‘B’ rated accounts so that cost of default is recovered. Because of higher rate of default, ‘B’ rated accounts would be priced at higher level than that of ‘A’ rated accounts.

Besides, pricing of loans and advances, this also helps in estimating defaults that the credit portfolio of the Bank may generate if the entire portfolio is rated and default probability is available for all rating categories. This also helps in assessing minimum capital required.

This is the basic approach that financial institutions/banks have adopted the world over in managing credit portfolio as it helps them to design a credit portfolio with a desired level of defaults and expected profits against available capital. This also has a bearing on the capital market and share prices of banks/FIs. The desire to have the portfolio rated and actively manage the credit portfolio stems from this basic need.

Regulatory authority, i.e., Reserve Bank of India has also issued necessary instructions and guidance notes emphasising upon banks to apply credit rating to their borrower accounts and classify them rating category-wise. They have also advised banks to develop and maintain necessary data on defaults of borrowers rating category wise. This would also help to manage credit portfolio in a proactive manner and to have a prior estimate of expected defaults, expected contribution and capital requirements to maintain the portfolio.

ii. Credit Rating – Approach to it :

In order to develop our capability to actively manage our credit portfolio, one must have in place the following:

a. Credit Rating Model (or models for different categories of loans and advances)

b. Develop and maintain necessary data on defaults of borrowers rating category, wise, i.e., ‘Rating Migration’.

a. Credit Rating Model :

A credit rating model essentially differentiates borrowers, based on degree of stability in terms of top line (e.g., sales) and bottom-line (net profit) revenue generation. This is because where uncertainty in revenue generation in a business is more, chances of failing in keeping financial commitments to the rest of the world is also more. Where revenue generation is stable over a given period, uncertainty or risk associated is zero.

For example, cash generation from an investment in Government. Securities are absolutely stable and hence risk associated with such investment is also non-existent. This would also mean that an ‘A’ rated borrower would have more stable revenue generation than that of a ‘B’ rated borrower and an ‘A++’ rated borrower’s revenue generation would be more stable than that of ‘A’ rated borrower.

It may also be clarified that rating has nothing to do with profitability. A highly profitable company may have higher level of uncertainties in revenue generation and therefore may be rated lower than a borrower with a relatively lower profitability but having more stable revenue generation. To cite the example of Government Securities again, we all know that returns on them is one of the lowest but are rated highest as stability in cash flow is absolute.

In developing a rating model therefore, factors that have an impact on the stability of revenue generation are relied upon. In fact, there is several rating models with various levels of complexities and require data sets that could be fairly extensive and cover few years.

The issue of simplicity and its user- friendliness are also very crucial in designing a model. A balance is struck keeping in view the need of both these aspects. As such, a purely simple model very easy to use may not be feasible as such a model may not be able to capture stability of revenue generation of the borrower and may not be acceptable.

This can be explained further. As in case of students, they are assessed against a set of standards using appropriate testing methods. If a College or Board, in order to show a better result, lowers its standards of testing and thereby show higher level of 1st division students, we know that the market assesses the standard of the college at a lower level. Even really better students suffer as they are rated in relation to the standard of the college. In the world of rating too this market behaviour is clearly visible. The standard of rating is assessed in the market through rating migration.

b. Rating Migration:

Rating migration is change in the rating of a borrower over a period of time when rated on the same standard or model. For example, say a borrower M/s. XYZ Ltd. is rated as B+ based on its position as on 31-3-02. The same company is again rated as on 31-3-03 based on its position as on that date, its rating, based on the same model, say comes to B. Then we say that the rating of the account has migrated from B+ to B over one year period.

As in case of rating of borrower, rating migration of a single account also does not convey much. It becomes useful when migration of a large number of accounts of similar rating is observed. Say, we have 100 ‘A’ rated borrowers as on 31st March 2008.

When these accounts are rated again as on 31st March 2009, i.e., after one year, typically we may find new ratings as given below:

This migration table implies that ‘A’ rated borrower would have 2% default probability. This is based on one-year data only. When this observation is collated over a number of years, we would have a fairly reasonable estimation of default probability.

This migration pattern of ‘A’ rated borrowers should also compare well with standard migration patterns published by rating agencies. This means that if usually ‘A’ rated borrowers show 0.2% default probability in terms of standard migration pattern observed, and rating migration as per the model records 2% default for ‘A’ category borrowers, then regulatory authorities, rating agencies and market will assume a lower rating for all borrowers rated ‘A’ under that model. The rating equivalent would be considered say B+, if default probability of B+ rated borrowers happens to be 2% in terms of standard rating models. This is known as mapping with market standards.

Acceptability of a rating model is an issue with the regulatory authorities, rating agencies and other market watchdogs and is tested based on two counts:

1. Whether relevant factors (i.e., risk drivers) have been taken into account in the model covering standard rating factors in the areas of management, financials, past conduct, business-related issues, industry, etc.

2. Whether rating migration developed based on the model maps fairly well with market standards, i.e., rating migration pattern published by rating agencies.

Many of the international banks have adopted credit risk models for evaluation of credit portfolio. The credit risk models offer banks framework for examining credit risk exposures, across geographical locations and product lines in a timely manner, centralising data and analysing marginal and absolute contributions to risk. The models also provide estimates of credit risk (unexpected loss), which reflect individual portfolio composition.

A few of them are mentioned here:

i. The Altman’s Z score forecasts the probability of a company entering bankruptcy within a 12- month period. The model combines five financial ratios using reported accounting information and equity values to produce an objective measure of borrower’s financial health.

ii. J.P. Morgan has developed a portfolio model ‘Credit Metrics’ for evaluating credit risks. The model basically focusses on estimating the volatility in the value of assets caused by variations in the quality of assets. The volatility is computed by tracking the probability that the borrower might migrate from one rating category to another (downgrade or upgrade).

Thus, the value of loans can change over time, reflecting migration of the borrowers to a different risk grade. The model can be used for promoting transparency in credit risk, establishing benchmark for credit risk measurement and estimating economic capital for credit risk under RAROC framework.

iii. Credit Suesse developed a statistical method for measuring and accounting for credit risk, which is known as Credit Risk+. The model is based on actuarial calculation of expected default rates and unexpected losses from default.

Banks should have a comprehensive risk scoring/rating system that serves as a single-point indicator of diverse risk factors of a borrower/counterparty and for taking credit decisions in a consistent manner. A substantial degree of standardisation is required in ratings across borrowers.

The risk rating system should be designed to reveal the overall risk of lending, critical input for setting pricing and non-price terms of loans as also present meaningful information for review and management of loan portfolio. The risk rating, in short, should reflect the underlying credit risk of the loan book. The rating exercise should also facilitate the credit granting authorities some comfort in its knowledge of loan quality at any moment of time.

The risk rating system should be drawn up in a structured manner, incorporating, inter alia, financial analysis, projections and sensitivity, industrial and management risks. The banks may use financial ratios and operational parameters, as also qualitative aspects of management and industry characteristics. Such system should also be periodically reviewed.

Essay # 6. Control and Monitoring of Credit Risk :

Risk taking through lending activities needs to be supported by a very effective control and monitoring mechanism, firstly because this activity is widespread, and secondly, because of very high share of credit risk in the total risk taking activity of a bank.

An elaborate and well-communicated policy at transaction level that articulates guidelines for risk taking, procedural guidelines and an effective monitoring system is necessary. This is also necessary to achieve the desired portfolio. Active portfolio management is required to keep up with the dynamics of the economy. It is also necessary to monitor it.

Consequently, credit risk control and monitoring is directed both at transaction level and portfolio level.

It must be mentioned here that an appropriate credit information system is basic prerequisite for effective control and monitoring. A comprehensive and detailed MIS and CIS is the backbone for an effective CRM System. There is need to review the existing MIS available from HO and branches and the applicability of data for analysis purposes. A detailed MIS and CIS structure should be set up and enforced for future data requirements.

Essay # Essay # 7. Credit Risk Policies and Guidelines at Transaction Level :

The instruments of Credit Risk Management at transaction level are:

i. Credit Appraisal Process,

ii. Risk Analysis Process,

iii. Credit Audit and Loan Review, and 

iv. Monitoring Process.

There is a need to constantly improve the efficiency for each of these processes in objectively identifying the credit quality of borrowers, enhancing default analysis, capturing the risk elements adequately for future reference and providing an early warning signal for deterioration in credit risk of borrowers.

Credit risk taking policy and guidelines at transaction level should be clearly articulated in the Bank’s Loan Policy Document approved by the Board.

Standards and guidelines should be outlined for:

i. Delegation of Powers

ii. Credit Appraisals

iii. Rating Standards and Benchmarks (derived from the Risk Rating System)

iv. Pricing Strategy

v. Loan Review Mechanism.

Credit Approving Authority :

Each Bank should have a carefully formulated scheme of delegation of powers. The banks should also evolve multi-tier credit approving system where the loan proposals are approved by an ‘Approval Grid’ or a ‘Committee’. The ‘Grid’ or ‘Committee’, comprising at least 3 or 4 officers, may approve the credit facilities above a specified limit and invariably one officer should represent the CRMD, who has no volume and profit targets.

The spirit of the credit approving system may be that no credit proposals should be approved or recommended to higher authorities, if majority members of the ‘Approval Grid’ or ‘Committee’ do not agree on the creditworthiness of the borrower. In case of disagreement, the specific views of the dissenting member/s should be recorded.

Credit Appraisal :

Credit appraisal guidelines include borrower standards, procedures for analyzing credit requirements and risk factors, policies on standards for presentation of credit proposals, financial covenants, rating standards and benchmarks etc. This brings a uniformity of approach in credit risk taking activity across the organisation. Credit appraisal guidelines may include risk monitoring and evaluation of assets at transaction level, pricing of loans, regulatory/legal compliance, etc.

Prudential Limits :

Prudential limits serve the purpose of limiting credit risk. There are several aspects for which prudential limits may be specified.

They may include:

(a) Prudential limits for financial and profitability ratios such as current ratio, debt equity and return on capital or return on assets, etc., debt service coverage ratio, etc.

(b) Prudential limits for credit exposure

(c) Prudential limits for asset concentration

(d) Prudential limits for large exposures

(e) Prudential limit for maturity profile of the loan book.

Prudential limits may have flexibility for deviations. The conditions subject to which deviations are permitted and the authority thereof should also be clearly spelt out in the Loan Policy.

Rating Standards and Benchmarks :

The credit risk assessment exercise should be repeated bi-annually (or even at shorter intervals for low quality customers) and should be delinked invariably from the regular renewal exercise. The updating of the credit ratings should be undertaken normally at quarterly intervals or at least a half-yearly intervals, in order to gauge the asset quality at periodic intervals.

Rating changes have implication at portfolio level. Variations in the ratings of borrowers over time indicate changes in credit quality and expected loan losses from the credit portfolio. Thus, if the rating system is to be meaningful, the credit quality reports should signal changes in expected loan losses.

In order to ensure the consistency and accuracy of internal ratings, the responsibility for setting or confirming such ratings should vest with the Loan Review function and examined by an independent Loan Review Group. The banks should undertake comprehensive study on migration (upward – lower to higher and downward – higher to lower) of borrowers in the ratings to add accuracy in expected loan loss calculations.

Risk Pricing :

The pricing strategy for credit products should move towards risk-based pricing to generate adequate risk adjusted returns on capital. The Credit Spread should have a bearing on expected loss rates and charges on capital.

Risk-return pricing is a fundamental tenet of risk management. In a risk-return setting, borrowers with weak financial position have high credit risk stake and should be priced high. Pricing of credit risk should have a bearing on the probability of default. Since probability of default is linked to risk rating, pricing of loans normally should be linked to rating. However, value of collateral, value of accounts, future business potential, portfolio/industry exposure and strategic reasons may also play important role in pricing.

There is, however, a need for comparing the prices quoted by competitors for borrowers having same rating/quality. Any attempt at price-cutting for market share would result in wrong pricing of risk.

Essay # 8. Credit Control and Monitoring at Portfolio Level :

Credit control and monitoring at portfolio level deals with the risk of a given portfolio, expected losses, requirement of risk capital, and impact of changing the portfolio mix on risk, expected losses and capital. It also deals with the marginal and absolute risk contribution of a new position and diversification benefits that come out of changing the mix. It also analyses factors that affect the portfolio’s risk profile.

The activities include:

i. Identification of portfolio credit weakness in advance – through credit quality migrations

ii. Move from measuring obligor specific risk associated with individual credit exposures to measuring concentration effects on the portfolio as a whole

iii. Evaluate exposure distribution over rating categories and stipulate quantitative ceilings on aggregate exposure in specified rating categories

iv. Evaluate rating wise distribution in various industries and set corresponding exposure limits to contain concentration risk

v. Move towards Credit Portfolio Value at Risk Models.

A framework of tracking the non-performing loans around the balance sheet date does not signal the quality of the entire loan book. A system for identification of credit weaknesses well in advance could be realised by tracking the migration (upward or downward) of borrowers from one rating scale to another.

This process would be meaningful only if the borrower wise ratings are updated at quarterly/half-yearly intervals. Data on movements within grading categories provide a useful insight into the nature and composition of portfolio.

Some measures to maintain the portfolio quality are:

1. Quantitative ceiling on aggregate exposure in specified rating categories.

2. Evaluation of rating wise distribution of borrowers in various industries, business segments, etc.

3. Industry wise and sector wise monitoring of exposure performance. Where portfolio exposure to a single industry is badly performing, the banks may increase the quality standards for that specific industry.

4. Target for probable defaults and provisioning requirements as a prudent planning exercise. For any deviation/s from the expected parameters, an exercise for restructuring of the portfolio should immediately be undertaken and if necessary, the entry-level criteria could be enhanced to insulate the portfolio from further deterioration.

5. Undertake rapid portfolio reviews, stress tests and scenario analysis when external environment undergoes rapid changes (e.g., volatility in the forex market, economic sanctions, changes in the fiscal/monetary policies, general slowdown of the economy, market risk events, extreme liquidity conditions, etc.). Based on the findings of stress test, prudential limits, quality standards, etc., may be revised.

6. Introduce discriminatory time schedules for review of borrowers.

The credit risk of a bank’s portfolio depends on both external and internal factors. The external factors are the state of the economy, wide swings in commodity/equity prices, foreign exchange rates and interest rates, trade restrictions, economic sanctions, government policies, etc.

The internal factors are deficiencies in loan policies/administration, absence of prudential credit concentration limits, inadequately defined lending limits, deficiencies in appraisal of borrowers’ financial position, excessive dependence on collaterals and inadequate risk-pricing, absence of loan review mechanism and post sanction surveillance, etc. Portfolio performance may be analysed to identify the causes and necessary remedial action.

Essay # 9. Active Credit Portfolio Management :

Motivation for active credit portfolio management comes from changing demand of traditional products and new business opportunities.

Change in demand of traditional products have arisen due to:

i. Less demand due to disintermediation

ii. More supply due to capital mobility

iii. Lower returns and increased importance of risk.

The motivation for active credit portfolio management also comes from new opportunities in the economy, such as:

i. Pass through certificates

ii. Syndicated lending

iii. Project/structured finance.

Essentially, new products have different risks from the traditional products.

In addition, banks have new tools to manage credit portfolio such as:

i. Secondary loan trading

ii. Securitisation

iii. Credit derivatives.

This calls for a business transformation plan – a gradual process with a well-articulated strategy and with a thorough understanding of markets and supported by:

i. Necessary infrastructure

ii. Appropriate policy development

iii. Human resource training

iv. Careful system selection

v. Continuous testing and refinement.

Essay # 10. Controlling Credit Risk through Loan Review Mechanism :

LRM is an effective tool for constantly evaluating the quality of loan book and to bring about qualitative improvements in credit administration. Loan Review Mechanism is used for large value accounts with responsibilities assigned in various areas such as, evaluating effectiveness of loan administration, maintaining the integrity of credit grading process, assessing portfolio quality, etc.

The main objectives of LRM are:

i. To identify promptly loans, which develop credit weaknesses and initiate timely corrective action

ii. To evaluate portfolio quality and isolate potential problem areas

iii. To provide information for determining adequacy of loan loss provision

iv. To assess the adequacy of and adherence to, loan policies and procedures, and to monitor compliance with relevant laws and regulations

v. To provide top management with information on credit administration, including credit sanction process, risk evaluation and post-sanction follow up.

Accurate and timely credit grading is one of the basic components of an effective LRM. Credit grading involves assessment of credit quality, identification of problem loans, and assignment of risk ratings. A proper Credit Grading System should support evaluating the portfolio quality and establishing a loan loss provisions. Given the importance and subject nature of credit rating, the credit ratings awarded by Credit Administration Department should be subjected to review by Loan Review Officers who are independent of loan administration.

Loan Review Policy should address the following issues:

Qualification and Independence:

The Loan Review Officers should have sound knowledge in credit appraisal, lending practices and loan policies of the bank. They should also be well-versed in the relevant laws/regulations that affect lending activities. The independence of Loan Review Officers should be ensured and the findings of the reviews should also be reported directly to the Board or Committee of the Board.

Frequency and Scope of Reviews :

The Loan Reviews are designed to provide feedback on effectiveness of credit sanction and to identify incipient deterioration in portfolio quality. Reviews of high value loans should be undertaken usually within three months of sanction/renewal or more frequently when factors indicate a potential for deterioration in the credit quality. The scope of the review should cover all loans above a cut-off limit.

In addition, banks should also target other accounts that present elevated risk characteristics. Although it is desirable to subject all loans above a cut off to LRM, at least 30-40% of the portfolio should be subjected to LRM in a year to provide reasonable assurance that all the major credit risks embedded in the balance sheet have been tracked.

Depth of Reviews :

The loan reviews should focus on:

i. Approval process

ii. Accuracy and timeliness of credit ratings assigned by loan officers

iii. Adherence to internal policies and procedures, and applicable laws/regulations

iv. Compliance with loan covenants

v. Post-sanction follows up

vi. Sufficiency of loan documentation

vii. Portfolio quality

viii. Recommendations for improving portfolio quality.

The findings of reviews should be discussed with line managers and the corrective actions should be elicited for all deficiencies. Deficiencies that remain unresolved should be reported to top management.

The banks should also evolve suitable framework for reporting and evaluating the quality of credit decisions taken by various functional groups. The quality of credit decisions should be evaluated within a reasonable time say 3-6 months, through a well-defined Loan Review Mechanism.

Review of Small Value Retail Loan Accounts :

Usually such assets are subjected to review on exception basis. Segments which show below average default performance are taken up for review and for putting in place remedial actions. Other segments are subjected to review on sample basis based on a predefined plan.

Essay # 11. Credit Risk Mitigation :

Credit risk mitigation is an essential part of credit risk management. This refers to the process through which credit risk is reduced or it is transferred to counterparty. Strategies for risk reduction at transaction level differ from that at portfolio level.

At transaction level banks use a number of techniques to mitigate the credit risks to which they are exposed. They are mostly traditional techniques and need no elaboration. They are, for example, exposures collateralised by first priority claims, either in whole or in part, with cash or securities, or an exposure guaranteed by a third party. Recent techniques include buying a credit derivative to offset credit risk at transaction level.

At portfolio level, asset securitisation, credit derivatives, etc., are used to mitigate risks in the portfolio. They are also used to achieve desired diversification in the portfolio as also to develop a portfolio with desired characteristics. It must be noted that while the use of CRM techniques reduces or transfers credit risk, it simultaneously may increase other risks such as legal, operational, liquidity and market risks.

Therefore, it is imperative that banks employ robust procedures and processes to control these risks as well. In fact, advantages of risk mitigation must be weighed against the risks acquired and its interaction with the bank’s overall risk profile.

Essay # 12. Securitisation :

Securitisation refers to a transaction where financial securities are issued against the cash flow generated from a pool of assets. Cash flow arising out of payment of interest and repayment of principal are used to service interest and repayment of financial securities. Usually a SPV – special purpose vehicle is created for the purpose. Originating bank – that is the bank who has originated the assets – transfers the ownership of such assets to the SPV. SPV issues financial securities and has the responsibility to service interest and repayments on such financial instruments.

In the process the originating bank transfers credit risk to the investors. A bank may also become an investor in a securitisation transaction and may acquire credit risk.

A traditional securitisation is a structure where the cash flow from an underlying pool of exposures is used to service at least two different stratified risk positions or tranches reflecting different degrees of credit risk. Payments to the investors depend upon the performance of the specified underlying exposures, as opposed to being derived from an obligation of the entity originating those exposures.

The stratified/tranched structures that characterise securitisations differ from ordinary senior/subordinated debt instruments in that junior securitization tranches can absorb losses without interrupting contractual payments to more senior tranches, whereas subordination in a senior/subordinated debt structure is a matter of priority of rights to the proceeds of liquidation.

A synthetic securitisation is a structure with at least two different stratified risk positions or tranches that reflect different degrees of credit risk where credit risk of an underlying pool of exposures is transferred, in whole or in part, through the use of funded (e.g., credit-linked notes) or unfunded (e.g. credit default swaps) credit derivatives or guarantees that serve to hedge the credit risk of the portfolio. Accordingly, the investors’ potential risk is dependent upon the performance of the underlying pool.

Securitisation exposures include asset-backed securities, mortgage-backed securities, credit enhancements, liquidity facilities, interest rate or currency swaps, credit derivatives and tranched cover. Underlying instruments in the pool being securitized may include loans, commitments, asset-backed and mortgage-backed securities, corporate bonds, equity securities, and private equity investments. The underlying pool may include one or more exposures.

An asset-backed commercial paper (ABCP) programme predominately issues commercial paper with an original maturity of one year or less that is backed by assets or other exposures held in a bankruptcy- remote, special purpose entity.

A clean-up call is an option that permits the securitisation exposures (e.g., asset-backed securities) to be called before all of the underlying exposures or securitisation exposures have been repaid. In the case of traditional securitisations, this is generally accomplished by repurchasing the remaining securitisation exposures once the pool balance or outstanding securities have fallen below some specified level. In the case of a synthetic transaction, the clean-up call may take the form of a clause that extinguishes the credit protection.

A credit enhancement is a contractual arrangement in which the bank retains or assumes a securitisation exposure and, in substance, provides some degree of added protection to other parties to the transaction.

In view of the various options that can be built up in a securitisation structure, it affords ways to add assets with specific characteristics in the portfolio. As an originator, one may also reduce a given exposure or a group of exposure to achieve desired portfolio.

Collateralised loan obligation is a form of securitisation, which is fairly popular in the banking industry.

This has been discussed in brief as follows:

Collateralised Loan Obligations :

Collateralised loan obligations (CLOs) are securitised pools of commercial loans. Attractive feature of CLO structures for banks is that, while preserving the origination of new lending opportunities, CLOs offer banks the opportunity to remove assets from the balance sheet by securitizing the credit risk into a tradable security – and thus potentially freeing up regulatory capital.

The collateral for the CLO (the underlying loans) usually resides in an SPV (special purpose vehicle) which in turn issues several levels of debt such as senior, mezzanine, and subordinated tranches. The cash flows generated from the collateral are dedicated to paying each debt tranche in order of seniority. Most senior CLO tranche can withstand high levels of default because of this cash flow prioritisation and as a result, senior CLO papers are high-quality assets (and rated as such) with mezzanine and subordinated tranches rated accordingly.

CLOs differ from credit linked notes in several ways:

i. A CLO will provide credit exposure to a diverse pool of credits whereas most credit-linked notes are linked to a single credit.

ii. CLOs may provide a true transfer of ownership of underlying assets, whereas credit-linked notes typically do not provide such a transfer.

iii. CLOs may enjoy a higher credit rating than that of the originating institution; whereas the rating of credit linked notes are effectively capped to the issuer level.

Credit derivative structures have been used within CLO transactions as a means to transfer credit risk or market risk between the originator and the SPV issuer.

Collateralised bond obligations (CBOs) are securitised pools of bonds. Collateralised debt obligations (CDOs) include both collateralised loan obligations and collateralised bond obligations. The same principles, as for CLOs, also apply to CBOs.

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