literature review on credit management in banks pdf

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Published: 09 December 2016

An empirical research on evaluating banks’ credit assessment of corporate customers

Sang-Bing Tsai 1 , 2 , 3 , 4 , 5 ,
Guodong Li 6 ,
Chia-Huei Wu 7 ,
Yuxiang Zheng 1 , 2 &
Jiangtao Wang 3

SpringerPlus volume 5 , Article number: 2088 ( 2016 ) Cite this article

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Under the rapid change of the global financial environment, the risk control of the credit granting is viewed as the foremost task to each bank. With the impact one by one from financial crisis and European debt crisis, the steady bank business is also facing the severe challenge. Banks approve the credits for their customers and then make money from the interest.

Case presentation

Credit granting is not only the primary job but also the main source of income. The quality of credit granting concerns not just the reclaims of creditor’s rights; it also affects the successful running of banks.

Discussion and Evaluation

To enhance the reliability and usefulness of bank credit risk assessment, we first will delve in the facets and indexes in the bank credit risk assessment. Then, we will examine the different dimensions of cause–effect relationships and correlations in the assessment process. Finally, the study focuses on how to raise the functions and benefits of the bank credit risk assessment.

Conclusions

In those five credit risk evaluation dimensions, A “optional capability” and D “competitiveness” are of high relation and high prominence among those dimensions, influencing other items obviously. By actively focusing on these two dimensions and improving their credit risk assessment ability will solve the foremost problems and also solve other facets of credit risk assessment problems at the same time.

Introduction

The so-called bank credit risk management is through the establishment of credit granting policies, instructions, and coordination between the different sections in the bank, such as the full supervision and control of customers’ credit investigation, choices of payment methods, confirmation of the credit limit, and reclaims of the sum of money, banks are guaranteed to retrieve the receivables back in time safely (Aebi et al. 2012 ; Benjamin and Charles 2014 ; Swami 2014 ).

However, there exists the phenomenon of “credit paradox” in the practice of credit risk management. This so called “credit paradox” is, on one hand, the risk management theory demands banks follow principles of the investment decentralization and diversification in bank credit risk management, to prevent the concentration of the credit authorization. Diversification is even more important and the golden rule to obey since particularly the traditional credit risk management model is lacking the effective credit risk hedge. On the other hand, in real practice, the bank loan business often shows that the diversification principle is not easy to put into practice because many banks do not abide by the diversification rule a lot on their loan business (Berger et al. 2009 ; Nuno and Manuela 2014 ; Mora 2014 ). There are several main reasons to cause “credit paradox” phenomenon, stated blow. (1) For most small–medium sized corporations without credit ratings, the credit situation is reveled by the long-term business partnership between the firms and banks. This way of partnership and information gained tends to make the banks execute loan business with the acquainted business clients. (2) Some banks would limit their loan business companies. Those firms whom the banks are familiar with in certain industry or in certain expertise are banks’ priorities. (3) Diversification of loan business tends to minimize the loan business to small-sized business, unfavorable to attain to the scale of benefits for banks on their loan business. (4) Sometimes the investment on the market would force banks to develop their loan business on certain limited sections or areas.

As to the credit risk assessment of banks, the accurate measurement of the risk is the basic premise. For the reasons stated above, it is extremely difficult to measure the credit risk accurately (Shipra and Yash 2014 ). So far some credit risk calculation models developed by JP Morgan and other institutes, such as Creditmetrics, CreditRlsk+, KMV models, are still disputable on their effectiveness and reliability. For the time being, there is still lacking effective measurement on credit risk (Aebi et al. 2012 ; Benjamin and Charles 2014 ; Shipra and Yash 2014 ).

Besides, for the related studies about the credit risk index in the past, there are two insufficient points. First, most studies are based on the hypothesis that indexes are independent, with no influences and cause–effect relationships on others. Second, some studies hold the same weight and hypothesis towards the assessment indexes. For solving the insufficiency in the previous studies and upgrading reliability and usefulness of the bank credit risk, this study adopts Decision Making Trial and Evaluation Laboratory (DEMATEL) to develop its theorizing. We first delve in the evaluation facets and indexes in the bank credit risk assessment. Then, we will examine the different dimensions of cause–effect relationships and correlations in the assessment process. Finally, the study focuses on how to raise the functions and benefits of the bank credit risk assessment.

Literature review

Bank credit risk and risk management.

The credit risk has been the most important management issue to banks. The quality of credit risk management, good or bad, matters a lot to banks which absorb the financial risks in exchange of benefits as their essence of business. The credit risk is like as follows: the borrower or the business counterparties are unable to fulfill the duty of their contracts out of the deterioration and other factors from the entrepreneurs (such as entanglement between firms); therefore this causes the risk of agreement violation and the loss of money. Generally, from different objects and behaviors, the credit risk could be further divided into two types: (1) lending risk, also called, issuer risk. This type of risk is duo to the violation of agreement when borrowers or bond issuers do not repay their debts or their credits get deteriorated, causing the money loss. Lending risk or issuer risk are often correlated to borrowers and bond issuers’ debt credit situations, and correlated to the risk sensitiveness degree of the financial products. (2) The second credit risk is counterparty risk; it could be further divided into two risks: settlement risk and pre-settlement risk. Settlement risk is the risk that counterparties do not fulfill their contract duties in the due settlement time and cause the loss of the equality principal to the bank. Pre-settlement risk is the risk that counterparties violate the agreement before the final settlement day and cause the risk of contract violation to the bank.

The bank credit risk management organizations and functions may appear in different forms. However, the bank should ensure the official positions and related authorities work independently and attributably, not just focusing on the superficial independency, to reach the goal of credit risk management and supervision, such as (Aebi et al. 2012 ; Jiang and Lo 2014 ; Nuno and Manuela 2014 ; Swami 2014 ):

Business functions should be independent from credit granting/verification functions to avoid the interest conflict.

Credit verification functions should be independent from credit granting functions to make sure the credit result report objective and just.

Accounting functions should be independent from credit granting/verification functions and business functions to avoid fraud and malpractice.

The unit responsible for designing, establishing, or executing the credit risk measurement system should be independent from the credit granting functions to keep this unit free of other interruptions.

The office worker in charge of verifying the credit risk measurement system should be different from the office worker responsible for designing or choosing the credit risk measurement system to lower the possibility of making errors from the credit risk measurement system.

The authorities should obey the regulations to restrict the interested parties in the bank.

Re-check the credit granting workers of interest in the bank, such as the credit granting of the general manager and the high-ranked officer.

Regularly (at least per year) check the strategies and related policies of the bank credit risk management to confirm that the high-ranked managers carry out the regulations successfully and to make sure the credit granting in accordance with those strategies and related policies. This is then to make the high-ranked managers ultimately responsible for establishing and maintaining the appropriate and effective credit risk management mechanism.

Make regular inspection on the bank management information and reflect on the correct credit risk strategies to guarantee the suitability and sufficiency of the bank capital.

The bank credit risk evaluation methods

For the past 20 years, the development of international bank credit risk management and evaluation has been through the several phases as follows:

Influenced by the debt crisis at 1980s, banks mostly began to focus on the preventative measures and management against the credit risk. Thus came out the result of the birth of “Basel Accord” which was a kind of vague analysis of the bank credit risk; through the adopting of different weights on different assets, this agreement quantified the risks.

Since 1990s some major banks acknowledged the fact that the credit risk was still the key factor in financial risks and they began to concern about the problems of the credit risk measurement, trying to establish the internal method and model for measuring the credit risk. Among those models, the credit risk management system “Credit Metrics” by J.P. Morgan obtained the widest attention.

After the outbreak of Asia financial crisis in 1997, some new phenomenon appeared in the global financial risk. The loss was not necessarily caused by single risk but by the mixture of the credit risk and the market risk etc. Financial crisis motivated people in the banking industry to value the mixture model of the market risk with the credit risk and to focus on the quantification problems of the operation risk. From this phase on, the comprehensive risk management model attained to people’s heed.

Within the traditional credit risk management, the main methods include the Expert System, Internal Ratings Grading Model for Loans, and Z Rating Model. Nevertheless, the modern development of banking makes those methods obsolete and inaccurate. With the advance of modern science and technology and with the enhancement of the management of the market risks plus other risks, modern credit risk management has also been lifted to the certain level. Therefore there appear some credit risk quantification management models such as “Creditmetrics”, “KMV”, “Creditrisk+” models. These models measuring the credit risk still arouse disputes over their effectiveness and reliability. Hence, in all respects, it is still lacking an effective calculating measure to assess the credit risk (Jiang and Lo 2014 ; Nuno and Manuela 2014 ; Swami 2014 ).

According to Dinh and Kleimeier ( 2007 ), the determination of loans does not depend on the borrower’s income or the amount of collateral, but rather on the qualitative analysis (of, for example, the borrower’s personality, reputation, or social status). Because the maintenance of social credit relationships is expensive, banks typically adopt the credit scoring model to quantitatively analyze a borrower’s credit situation to determine loans and identify whether a borrower can obey the contract. Banks’ credit assessment of corporate customers is a multiple-criteria decision-making problem in which various elements are comprehensively assessed. The construction of an effective credit assessment model requires that credit staff possess sufficient professional knowledge and practical experience. Previous credit assessment studies have mostly analyzed the opinion of a group of credit staff by using a single precise value, which cannot fully describe the actual distribution of credit staff opinions and tends to diminish minority and peculiar opinions. Therefore, precise values are inapplicable in actual decision environments and constructed credit assessment models do not possess the features of anti-catastrophism and sensitivity, which are the criteria of a superior assessment system (Hsieh 2003 ). Srinivasan and Kim ( 1988 ) stated that credit assessment can be conducted using theory-based scientific and objective methods. The experience of credit decision managers and senior credit staff responsible for credit assessment can be applied to credit assessment models for determining credit categorization and rating weights (Chiou and Shen 2011 ; Lee et al. 2016 ).

Research method

Based on the literature regarding the banks’ approaches and principles of corporate customer credit rating, this study developed five assessment dimensions and 25 criteria, with the definitions listed in Table 1 .

DEMATEL model

This study adopted the DEMATEL, which was proposed by Gabus and Fontela who were employed in the Battelle Memorial Institute of Geneva (Gabus and Fontela 1973 ; Fontela and Gabus 1976 ; Lee et al. 2014a , b ; Guo and Tsai 2015 ; Guo et al. 2015 ; Gandhi et al. 2016 ). At the initial stage, the DEMATEL was used to solve difficult and complex problems such as racial, hunger-related, environmental, and energy-related problems (Hu 2003 ; Huang 2013 ; Tsai and Xue 2013 ; Tsai et al. 2014 , 2015 , 2016a ; Qu et al. 2015 ). In this study, the DEMATEL was adopted to establish a relationship structure comprising elements used for banks’ credit assessment of corporate customers. When a bank assesses corporate customers, the relationship and degree of influence among the assessment elements are problems common to bank managers. In other words, when a bank manager intends to improve numerous decision-making elements, the optimal approach is to search for the most critical element that influences all other elements.

The DEMATEL structure and calculation steps are summarized and explained in the following sections (Yang and Tzeng 2011 ; Wu et al. 2013 ; Liu et al. 2015 ; Zhang et al. 2015 ; Tsai 2016 ; Tsai et al 2016b ; Zhou et al. 2016 ).

The six steps of DEMATEL analysis were implemented in this study:

Understanding and defining elements

Problems were thoroughly understood, and elements were determined and defined in a complex system through in-depth interviews, a literature review, brainstorming, or the collection of expert opinions.

Determining the correlation among elements and establishing measurement scales

Based on the relationship among elements, a scale of influence degree was developed for pair-wise comparisons. Specifically, each interviewee’s cognition of each aspect’s influence degree was assessed through the pair-wise comparison of aspects (elements). In the assessment scale, 0, 1, 2, 3, and 4 denoted no influence , low influence , moderate influence , high influence , and excessively high influence among the aspects (elements), respectively.

Constructing a direct-relation matrix

The number of elements was denoted as n . Expert opinions were collected by conducting a questionnaire survey. Elements were compared in pairs based on their relationship and degree of influence. Therefore, an n × n direct-relation matrix (denoted as X ) was obtained, in which x ij indicated the influence degree of element i on element j , and the diagonal elements x ii were set as 0.

Direct-relation matrix X

The symbolic matrix S was established, representing the positive and negative influences (denoted as + and −, respectively).

Calculating a normalized direct-relation matrix

Through the calculation of Eqs. ( 2 ) and ( 3 ), the direct-relation matrix was multiplied by λ to generate the normalized direct-relation matrix N .

In addition, DEMATEL analysis assumes that the sum of at least one row of i must meet the requirement presented in Eq. ( 4 ).

Therefore, the substochastic matrix was computed using the normalized direct-relation matrix N .

where O represented a null matrix and I an identity matrix.

Calculating a direct/indirect relation matrix

When normalized direct-relation matrix N met the requirement of Eq. ( 5 ), the direct/ indirect relation matrix T , also named the total-relation matrix, was obtained using Eq. ( 6 ). The indirect relation matrix H , also called the total-indirect-relation matrix, was obtained using Eq. ( 7 ).

Let t ij be the assessment element in the direct/indirect relation matrix T , and i , \(j = 1,2, \ldots ,n\) . The sum of rows and that of columns of T were calculated using Eqs. ( 8 ) and ( 9 ). The sum of row i was denoted as D i , signifying that the assessment element i was the factor that influenced other assessment elements; R j represented the sum of column j , indicating that the assessment element i was the result influenced by other assessment elements. Both D i and R j , which were obtained using the direct/indirect relation matrix T , involved direct and indirect influences.

Illustrating the causal diagram

In the causal diagram, ( D k + R k , D k − R k ) represented the horizontal and vertical axes. The mean value and 0.0 were used as the dividing points on the horizontal axis ( D k + R k ) and vertical axis ( D k − R k ), respectively, dividing the causal diagram into four quadrants. The values of ( D k + R k ) on the horizontal axis were defined as prominence , and \(k = i = j = 1,2, \ldots ,n\) , indicated the total degree to which an element exerted influence on and was influenced by other elements. Therefore, ( D k + R k ) showed the degree to which element k was at the core of all problems. In addition, the values of ( D k − R k ) on the vertical axis were defined as relation , representing the difference in the degree to which an element exerted influence on and was influenced by other elements. Thus, ( D k − R k ) showed the causal degree of element k in all problems. If the value of ( D k − R k ) was positive, the element tended to be a cause; if the value was negative, the element tended to be a result (Hung 2011 ; Hsu et al. 2013 ; Ren et al. 2013 ; Gandhi et al. 2015 ).

Results and discussion

Questionnaires.

The five dimensions and 25 elements for banks’ credit assessment of corporate customers were used as items in the DEMATEL expert questionnaire. The questionnaire survey was administered to bank managers in Taiwan. The details are described as follows.

Questionnaires were distributed to 18 Taiwan bank credit managers with more than 20 years of work experience. The DEMATEL questionnaires were distributed between March 16, 2015, and April 30, 2015. The measurement scale was a 5-point scale, with 4 representing maximal influence and 0 representing no influence. The scores between these two values were sequential ratings based on value. The author visited each expert in person, explained the content of the questionnaire, and requested each expert to complete the questionnaire. Overall, 18 questionnaires were distributed and returned. The valid return rate was 100%.

This study used Matlab software to calculate. The scores from the 18 experts were averaged and rounded to one decimal place to create a table of five criteria, as shown in Table 2 .

Next, the normalized direct-relation matrix was calculated using column vectors and maximums as benchmarks for normalization. The reciprocal of the maximum value within the sum of each column was the λ value. Using Eqs. ( 2 ) and ( 3 ), the direct-relation matrix X was multiplied by the λ value to obtain the normalized direct-relation matrix N. The influence coefficient was rounded to two decimal places (Table 3 ).

Equations ( 4 ), ( 5 ), ( 6 ), ( 7 ) were then used to calculate the total-relation matrix T, as shown in Table 4 .

Equations ( 8 ) and ( 9 ) were used to calculate value Di of each column and value Rj of each row to obtain prominence (D + R) and relation (D − R), as shown in Table 4 . In addition, the five dimensions were drawn into a figure with prominence as the horizontal axis and relation as the vertical axis, as shown in Fig. 1 .

DEMATEL distribution diagram for the five dimensions

From the results of Table 5 and Fig. 1 , the cause–effect relationships and correlations among five evaluation dimensions are interpreted as follows.

High relation and high prominence: This category contained A “Operational capability” and D “Competitiveness”. These two dimensions were properties in the cause category and were core influences on the other dimensions. This indicates that these were driving factors and critical problem-solving factors.

Low relation and high prominence: This category contained B “Repayment ability” and C “Financing capacity”. These two dimensions were in the effect category and were influenced by the other properties. Although B, C were a property that required improvement, it could not be directly improved because it was in the effect class. Therefore, B, C was relatively irrelevant.

Low relation and low prominence: This category contained E “Response ability”. This dimension was influenced by other properties. However, the influences were small. This dimension that these properties were relatively independent.

All in all, in those five credit risk evaluation dimensions above, A “optional capability” and D “competitiveness” are of high relation and high prominence among those dimensions, influencing other items obviously. By actively focusing on these two dimensions and improving their credit risk assessment ability will solve the foremost problems and also solve other facets of credit risk assessment problems at the same time. Thus we suggest the bank corporations pay huge efforts to improve the credit risk assessment and censorship of these two facets, to upgrade the results of the credit risk assessment immediately.

What we will explain is A “optional capability” and D “competitiveness” are two base dimensions for corporation’s competitive ability, competitive advance, and profit gaining ability. If the corporations’ operational capability and competitive ability are the priority to get upgraded, then the corporation’s repayment ability and financing capacity would also arise naturally.

With the liberalization and globalization of financial development, innovative financial activities flourishing, and the banking business more and more complicated, the financial system risks also gradually increase with time. To effectively adjust to the rapid change of the financial environment, main countries in the world all devote to carrying out financial reforms. Through reflections on financial supervision system and financial regulations, through the improvement of financial credit risk assessment techniques, the banks are urged to sharpen their risk management and corporation administration, to derive a robust financial system and to enhance the country’s financial competitive advantage.

For resolving the insufficiency of the former studies, this study is developed with DEMATEL Model, to increase the reliability and usefulness of the bank credit risk. To enhance the reliability and usefulness of bank credit risk assessment, we first will delve in the facets and indexes in the bank credit risk assessment. Then, we will examine the different dimensions of cause–effect relationships and correlations in the assessment process. Finally, the study focuses on how to raise the functions and benefits of the bank credit risk assessment.

In those five credit risk evaluation dimensions above, A “optional capability” and D “competitiveness” are of high relation and high prominence among those dimensions, influencing other items obviously. By actively focusing on these two dimensions and improving their credit risk assessment ability will solve the foremost problems and also solve other facets of credit risk assessment problems at the same time. Thus we suggest the bank corporations pay huge efforts to improve the credit risk assessment and censorship of these two facets, to upgrade the results of the credit risk assessment immediately.

We suggest the follow-up studies could adopt DEMATEL model and study the cases from other different countries and areas, to discuss the bank credit risk assessment problems and make a comparative study over miscellaneous areas. Other research methods are also recommended to develop other evaluation index system and to make comparisons.

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Authors’ contributions

Writing: S-BT; providing case and idea: C-HW, S-BT; providing revised advice: GL, C-HW, YZ, JW. All authors read and approved the final manuscript.

Acknowledgements

This work was supported by National Social Science Fund of China (No. 12BYJ125), Provincial Nature Science Foundation of Guangdong (Nos. 2015A030310271 and 2015A030313679), Academic Scientific Research Foundation for High-level Researcher, University of Electronic Science Technology of China, Zhongshan Institute (No. 415YKQ08), Tianjin philosophy and social science planning project (No. TJGL-028), The Fundamental Research Funds for the Central Universities (No. ZXH2012N002).

Competing interests

The authors declare that they have no competing interests.

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Law School, Nankai University, Tianjin, 300071, China

Zhongshan Institute, University of Electronic Science and Technology of China, Zhongshan, 528400, Guangdong, China

Sang-Bing Tsai & Jiangtao Wang

Sang-Bing Tsai

School of Business, Dalian University of Technology, Panjin, 124221, China

Economics and Management College, Civil Aviation University of China, Tianjin, 300300, China

Institute of Service Industries and Management, Minghsin University of Science Technology, Hsinchu, 304, Taiwan

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Tsai, SB., Li, G., Wu, CH. et al. An empirical research on evaluating banks’ credit assessment of corporate customers. SpringerPlus 5 , 2088 (2016). https://doi.org/10.1186/s40064-016-3774-0

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DOI : https://doi.org/10.1186/s40064-016-3774-0

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Decision-Making Trial and Evaluation Laboratory (DEMATEL)
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This study investigated corporate size and financial performance of deposit money banks in Nigeria for the period 2010 to 2019. The study employed ex post facto and correlational research design and the population comprised of all banks listed on the Nigerian Stock Exchange for the period under review. The sample consisted of ten (10) banks after data filtration using simple random sampling technique. The study collected data from secondary sources mostly the sampled banks financial statements and Central Bank of Nigeria Statistical Bulletin. The secondary data obtained was analysed with descriptive and inferential statistics. The inferential statistics employed multiple regression analysis (parsimonious error correction model). The result showed a positive and significant relationship between bank size and return on assets of deposit money banks in Nigeria. The paper concluded that banks size positively influences the financial performance of deposit money banks. Therefore, the paper recommended amongst others that deposit money banks in Nigeria should improve their assets and level of capitalization so as to improve their lending capability and hence financial performance.

This paper analyzed the impact of CAMEL indexes on bank performance and how this impact varies between Islamic private commercial banks (PCBs) and conventional private commercial banks (PCBs). The size of the sample was 23 banks listed with the Dhaka Stock exchange(DSE) including 17 conventional PCBs and 6 Islamic PCBs in Bangladesh with 125 observations from 2015 to 2019. In analyzing the impact of CAMEL on profitability, Capital adequacy was measured by the ratio of total capital to risk-weighted assets, asset quality was measured by non-performing loan ratio, management quality was measured by the cost to income ratio, earnings quality was measured by net interest margin and liquidity by loan to total deposit ratio. Five CAMEL parameters along with bank size as a control variable were regressed against profitability. A dummy variable (Bank Type) was created to moderate the relationship of CAMEL parameters and profitability between conventional and Islamic banks. The analysis was conducted using descriptive analysis, correlation analysis, and multiple regression analysis. The findings of descriptive analysis showed that on average Islamic banks are better in asset quality and management quality while conventional banks in capital adequacy, earnings quality, and liquidity. The results of the regression analysis revealed that asset quality and management quality had a significant negative impact on profitability while earnings quality had a significant positive impact on profitability. The other two CAMEL parameters capital adequacy and liquidity hada negative but insignificant impact on profitability. The study also revealed that profitability measured by return on asset (ROA) was significantly higher for conventional banks. The study suggested that Islamic banks should focus on increasing net investment income to increase profitability and stay competitive with conventional banks.

Microfinance institutions provide financial services in small scale to the unbanked who are unable to receive credit from the formal banking sector and as well as other standard financial systems. However, over the years, the financial stability of microfinance banks in Kenya has attracted key consideration from policy makers. The study sought to assess the moderating effect of interest rates on the relationship between firm characteristics and financial stability of Microfinance Banks in Kenya. The study is guided by Financial Intermediation Theory. The study targeted the 13 microfinance banks in Kenya, hence a census study. The study concluded that interest rates had significant moderating effect (β=34.223, p=0.000) on the relationship between firm characteristics and financial stability of Microfinance Banks in Kenya. The study also presented a workable empirical model on firm characteristics, interest rates and financial stability as it statistically established significance on the nexus between these variables. The study recommends that the setting of interest rates should be guided by the underlying economic conditions of the country

IOSR Journal of Business and Management (IOSR-JBM)

Wakara Nyabakora

Abstract: Background-The study examines the impact of macroeconomic variables on nonperforming loans for Tanzanian banking sector from 2013 to 2019. Macroeconomic variables include Gross Domestic Product growth rate, Money supply Rates, inflation rate, exchange rate, and interest rate. We have chosen the area under study due to the fact that, the research in this phenomenon is still at the preliminary level in Sub Sahara Africa in such a way that, no studies regarding the topic have done on the study area at that period regardless of the high economic growth of the country in the Sub Sahara Africa and Africa in general (World Bank 2017), whose economy (regarding business capital provision) depends most on commercial banks rather than the capital market (Dar es Salaam Stock Exchange) which until now the market has only 27 listed firms (Nyabakora, W. et. el. 2020). With this regard, the commercial banks have to be protected from the unknown harmful factors and this will be attained only if we know how the factors affect the banks’ performance. Material and Methods: Our study employed Tanzanian banking sector’s secondary data from the central bank of Tanzania, the Dare es salaam stock exchange, and the empirical evidences from the works of other researchers, from 2013 to 2019, using Panel data regressions and correlation in the analysis. Results: The results found a positive impact of macroeconomic variables proxied by interest rate, and exchange rate, on non performing loans. However, the results found a negative impact of gross domestic product growth rates, the rate of money supply, and inflation rates, to non performing loans. Key words: Macroeconomic, Non-Performing Loan, Tanzania, GDP, Growth Rates

Jehona Shkodra

The determinant of the credit risk of banks in a developing country have limited data to analyze and limited participation in literature. Determinants of credit risk are very important in order to define the non-performing loans (NPL) in Kosovo banking systems. Even though banking system in Kosovo is the newest in region, it is comparable with banking systems to all places in regions (Albania, Serbia, Montenegro, Macedonia, Bosnia and Herzegovina, etc.). The main purpose of this paper is to classify some factors that influence credit risk in commercial banks in Kosovo. The research includes seven commercial banks for the period 2006–2015. Data analysis and interpretation are processed with Statistical Program for Social Sciences SPSS v.19.0. The effect of variations in the determinants of credit risk exposure is based on using a multivariate panel regression model. Our empirical results suggest that a significant relationship exists between credit risk and the following variables: Profitability (ROE and ROA), Inefficiency (IE), Loans to deposit ratio (LDR), Credit growth (CG) and Deposit rate (DR), while variables of Solvency (SR) and Credit rate (CR) are not statistically significant in terms of credit risk

This paper seeks at investigating the relationship between the liquidity and the profitability of commercial banks in Nepal. Ten out of Twenty seven listed commercial banks were involved in the study covering the period from 2013 to 2019. This study is based on the secondary data, which are extracted from Bank Supervision Reports published by Nepal Rastra Bank and annual reports of the selected commercial banks. The liquidity indicators are credit-deposit ratio (CDR), cash-deposit ratio (CADR) and assets quality (AQ), while return on equity (ROE) and return on assets (ROA) are the proxies for profitability. By using Hausman test and thereafter fixed effects approach, the result showed that assets quality (AQ) has negative and significant relationship with return on assets (ROA) whereas it has positive and significant relationship with return on equity (ROE). Cash-deposit ratio (CADR) has positive and insignificant relationship with return on assets (ROA) and return on equity (ROE). However, the study reveals that credit-deposit (CDR) has positive but insignificant relationship with ROA and has negative and insignificant relationship with return on equity (ROE).

Kipkorir Kiptoo

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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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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Shailesh Rastogi, Arpita Sharma & Venkata Mrudula Bhimavarapu

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Geetanjali Pinto

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‘SR’ performed Abstract, Introduction, and Data methodology sections and was the major contributor; ‘AS’ performed Bibliometric and Network analysis and conceptual framework; ‘GP’ performed citation analysis and discussion section; ‘VMB’ collated data from the database and concluded the article. All authors read and approved the final manuscript.

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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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Bank performance
Profitability
Bibliometric analysis
Scientometric analysis

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seeks to explain how the outside economic factors influence the relationship between the credit risk and bank performance. Null Hypothesis: There is no relationship between credit risk management and bank performance. Alternate Hypothesis: Credit risk management has a relationship with the bank performance. Figure 3. 1: The conceptual model
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An empirical research on evaluating banks’ credit assessment of corporate customers

Case presentation

Discussion and Evaluation

Conclusions

Introduction

Literature review

The bank credit risk evaluation methods

Research method

DEMATEL model

Results and discussion

Authors’ contributions

Acknowledgements

Competing interests

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Credit Risk Management and Bank Performance: A Critical Literature Review

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

Introduction

Issues of performance and risk management of banks

Data and research methods

Determining the appropriate keywords for exploring the data

Initial search results and limiting criteria

Data analysis tools and techniques

Bibliometric and network analysis

Source of publications

Location and affiliation analysis

Key author analysis

Keyword analysis

Citation analysis

Page Rank analysis

Content analysis

Regulation and profitability of banks

Regulation and NPA (risk-taking of banks)

NPA (risk-taking) in banks and profitability

Triad and the performance of banks

Concentration banking and triad

Competition, the performance of banks, and triad

Transparency, corporate governance, and triad

Discussion of the findings

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

Research Implications of the study

Competition

No incentive to take risks

Conclusion of the study

Practical implications of the study

Limitations and scope for future research

Availability of data and materials

Abbreviations

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