thesis

research paper on credit management

Share on FacebookShare on Twitter

Information

• Author Services

Initiatives

You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

• Active Journals
• Find a Journal
• Proceedings Series
• For Authors
• For Reviewers
• For Editors
• For Librarians
• For Publishers
• For Societies
• For Conference Organizers
• Open Access Policy
• Institutional Open Access Program
• Special Issues Guidelines
• Editorial Process
• Research and Publication Ethics
• Article Processing Charges
• Testimonials
• Preprints.org
• SciProfiles
• Encyclopedia

Article Menu

• Subscribe SciFeed
• Recommended Articles
• Google Scholar
• on Google Scholar
• Table of Contents

Find support for a specific problem in the support section of our website.

Please let us know what you think of our products and services.

Visit our dedicated information section to learn more about MDPI.

JSmol Viewer

Examining the determinants of credit risk management and their relationship with the performance of commercial banks in nepal.

1. Introduction

1.1. justifications, 1.2. objectives of the research, 2. literature review and hypotheses development, 2.1. theoretical context, 2.1.1. agency theory, 2.1.2. institutional theory, 2.2. credit risk management and its determinants, 2.2.1. environmental risk, 2.2.2. credit appraisal measurements, 2.2.3. market risk analysis, 2.2.4. credit risk management and performance of commercial banks, 2.2.5. mediating role of credit risk management (crm) and commercial banks’ performance (cbp), 3. research methodology and data collection tools, 3.1. data collection tools/methods, 3.2. measurement scale, 4. analysis/results, 4.1. information on the respondents in detail, 4.2. measurements through partial least squares structural equation modeling (pls-sem), 4.2.1. an assessment of measurement model, 4.2.2. assessment of discriminant validity, 4.3. structural model, analysis of indirect effects through mediation, 5. results and discussion, 6. conclusions, author contributions, data availability statement, conflicts of interest.

• Abdullah, Mazni Asrida, Azlina Ahmad, Nor Azam Mat Nayan, Zubir Azhar, and Abd-Razak Ahmad. 2020. Credit risk assessment models of retail microfinancing: The case of a Malaysian national savings bank’s branch. International Journal of Financial Research 11: 73–83. [ Google Scholar ] [ CrossRef ]
• Ab-Hamid, Mohd Fahmee, Aisyah Abdul-Rahman, Mariani Abdul-Majid, and Hawati Janor. 2017. Bank market risk and efficiency of commercial banks in Malaysia. Journal Pengurusan 51: 249–59. [ Google Scholar ] [ CrossRef ]
• Abu Hussain, Hameeda, and Jasim Al-Ajmi. 2012. Risk management practices of conventional and Islamic banks in Bahrain. The Journal of Risk Finance 13: 215–39. [ Google Scholar ] [ CrossRef ]
• Abubakar, A., A. S. Sulaiman, B. Usman, and M. U. Mohammed. 2019. Credit risk management and financial performance of quoted deposit money banks in Nigeria. Journal of Finance, Accounting and Management 10: 57–74. [ Google Scholar ]
• Afolabi, Taofeek Sola, Tomola Marshal Obamuyi, and Tajudeen Egbetunde. 2020. Credit risk and financial performance: Evidence from microfinance banks in Nigeria. IOSR Journal of Economics and Finance 11: 8–15. [ Google Scholar ]
• Ahmadyan, Azam. 2018. Measuring credit risk management and its impact on bank performance in Iran. Marketing, and Branding Research 5: 168. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Ahmed, Sufi Faizan, and Qaisar Ali Malik. 2015. Credit risk management and loan performance: Empirical investigation of micro finance banks of Pakistan. International Journal of Economics and Financial Issues 5: 574–79. [ Google Scholar ]
• Al-Mekhlafi, Al-Baraa Abdulrahman, Ahmad Shahrul Nizam Isha, Nicholas Chileshe, Mohammed Abdulrab, Ahmed Farouk Kineber, and Muhammad Ajmal. 2021. Impact of safety culture implementation on driving performance among oil and gas tanker drivers: A partial least squares structural equation modelling (PLS-SEM) approach. Sustainability 13: 8886. [ Google Scholar ] [ CrossRef ]
• ALrfai, Mohammad Motasem, Danilah Binti Salleh, and Waeibrorheem Waemustafa. 2022. Empirical Examination of Credit Risk Determinant of Commercial Banks in Jordan. Risks 10: 85. [ Google Scholar ] [ CrossRef ]
• Alshiqi, Sevdie, and Arbana Sahiti. 2021. Risk management and profitability of commercial banks of Western Balkans countries of Kosovo, Albania, North Macedonia, and Serbia. Journal of Eastern European and Central Asian Research 8: 81–88. [ Google Scholar ] [ CrossRef ]
• Al-Tamimi, Hussain A. Hassan. 2002. Risk management practices: An empirical analysis of the UAE commercial banks. Finance India 16: 1045. [ Google Scholar ]
• Al-Tamimi, Hussein A. Hassan, and Faris Mohammed Al-Mazrooei. 2007. Banks’ risk management: A comparison study of UAE national and foreign banks. The Journal of Risk Finance 8: 394–409. [ Google Scholar ] [ CrossRef ]
• Annandale, David. 2001. Developing and evaluating environmental impact assessment systems for small developing countries. Impact Assessment and Project Appraisal 19: 187–93. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Anteneh, Hiwot. 2021. The Impact of Credit Risk Management on the Financial Performance of Ethiopian Commercial Banks. Master’s thesis, St. Mary’s University, San Antonio, TX, USA. [ Google Scholar ]
• Balampaki, Rashmi. 2021. Credit Risk Management and Performance of Commercial Banks. Master’s thesis, Department of Management, Central Department of Management, Kirtipur, Nepal. [ Google Scholar ]
• Bank for International Settlements. 1999. A New Capital Adequacy Framework . Basel: Committee on Banking Supervision. [ Google Scholar ]
• Banu, Mst Hasna, Md Sayaduzzaman, and Subhash Chandra Sil. 2021. The Impact of Credit Risk Management Indicators on Profitability Attributes: Evidence from the State-Owned Commercial Banks in Bangladesh. American Journal of Trade and Policy 8: 215–24. [ Google Scholar ] [ CrossRef ]
• Bernanke, Ben S. 1993. Credit in the Macroeconomy. Quarterly Review-Federal Reserve Bank of New York 18: 50. [ Google Scholar ]
• Bessis, Joel. 2011. Risk Management in Banking . New York: John Wiley & Sons. [ Google Scholar ]
• Bhattarai, Babu R., and Klaus Fischer. 2014. Human–tiger Panthera tigris conflict and its perception in Bardia National Park, Nepal. Oryx 48: 522–28. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Boahene, Samuel Hymore, Julius Dasah, and Samuel Kwaku Agyei. 2012. Credit risk and profitability of selected banks in Ghana. Research Journal of Finance and Accounting 3: 6–14. [ Google Scholar ]
• Boyd, A. 1993. How the industry has changed since deregulation. Personal Investment 11: 85–86. [ Google Scholar ]
• Brown, Ken, and Peter Moles. 2014. Credit risk management . Edinburgh: Edinburgh Business School Heriot-Watt University. [ Google Scholar ]
• Bryant, Kay. 1999. The Integration of Qualitative Factors into Expert Systems for Evaluating Agricultural Loans. Paper presented at the Australasian Conference on Information System, Wellington, New Zealand, December 1–3. [ Google Scholar ]
• Bryman, Alan. 2006. Integrating quantitative and qualitative research: How is it done? Qualitative Research 6: 97–113. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Byusa, Vincent, and David Nkusi. 2012. The effects of credit policy on bank performance: Evidence from selected Rwandan Commercial banks. Rwanda Journal 26: 116–19. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Campbell, Andrew. 2007. Bank insolvency and the problem of nonperforming loans. Journal of Banking Regulation 9: 25–45. [ Google Scholar ] [ CrossRef ]
• Caouette, John B., Jack B. Caouette, Edward I. Altman, and Paul Narayanan. 1998. Managing Credit Risk: TheNext Great Financial Challenge . New Jersey: John Wiley & Sons, Inc. [ Google Scholar ]
• Chandra, Amaresh, and R. K. Bhatt. 1998. Biochemical and physiological response to salicylic acid in relation to the systemic acquired resistance. Photosynthetica 35: 255–58. [ Google Scholar ] [ CrossRef ]
• Chavan, Pallavi, and Leonardo Gambacorta. 2016. Bank Lending and Loan Quality: The Case of India . Basel: Bank for International Settlements. [ Google Scholar ]
• Chege, Mwangi Leonard. 2021. Credit Management Practices and Loan Default in Agricultural Finance Corporation, Kenya. Master’s thesis, Kenyatta University, Nairobi, Kenya. [ Google Scholar ]
• Chen, Wei-Da, Yehning Chen, and Shu-Chun Huang. 2021. Liquidity risk and bank performance during financial crises. Journal of Financial Stability 56: 100906. [ Google Scholar ] [ CrossRef ]
• Cheng, LiMei, Takyi Kwabena Nsiah, Charles Ofori, and Abraham Lincoln Ayisi. 2020. Credit risk, operational risk, liquidity risk on profitability. A study on South Africa commercial banks. A PLS-SEM Analysis. Revista Argentina de Clínica Psicológica 29: 5. [ Google Scholar ]
• Chin, Wynne W., Barbara L. Marcolin, and Peter R. Newsted. 2003. A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study. Information Systems Research 14: 189–217. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Christoffersen, Peter. 2011. Elements of Financial Risk Management . Cambridge: Academic Press. [ Google Scholar ]
• Collier, Paul M., and Margaret Woods. 2011. A comparison of the local authority adoption of risk management in England and Australia. Australian Accounting Review 21: 111–23. [ Google Scholar ] [ CrossRef ]
• Consonni, Viviana, Davide Ballabio, and Roberto Todeschini. 2010. Evaluation of model predictive ability by external validation techniques. Journal of Chemometrics 24: 194–201. [ Google Scholar ] [ CrossRef ]
• Cortina, Jose M. 1993. What is coefficient alpha? An examination of theory and applications. Journal of Applied Psychology 78: 98. [ Google Scholar ] [ CrossRef ]
• Coulson, Andrea B., and Rob Dixon. 1995. Environmental risk and management strategy: The implications for financial institutions. International Journal of Bank Marketing 13: 22–29. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Coulson, Andrea B., and Vivienne Monks. 1999. Corporate environmental performance considerations within bank lending decisions. Eco-Management and Auditing: The Journal of Corporate Environmental Management 6: 1–10. [ Google Scholar ] [ CrossRef ]
• Cronbach, Lee J., and Paul E. Meehl. 1955. Construct validity in psychological tests. Psychological Bulletin 52: 281. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Dang, Van Cuong, Quang Khai Nguyen, and Xuan Hang Tran. 2022. Corruption, institutional quality and shadow economy in Asian countries. Applied Economics Letters , 1–6. [ Google Scholar ] [ CrossRef ]
• Das, Ankita, Jan Konietzko, and Nancy Bocken. 2022. How do companies measure and forecast environmental impacts when experimenting with circular business models? Sustainable Production and Consumption 29: 273–85. [ Google Scholar ] [ CrossRef ]
• Davari, A., and A. Rezazadeh. 2016. Structural Equation Modeling with PLS . Tehran: Jahad daneshgahi. [ Google Scholar ]
• Deng, Liurui, Yongbin Lv, Ye Liu, and Yiwen Zhao. 2021. Impact of fintech on bank risk-taking: Evidence from China. Risks 9: 99. [ Google Scholar ] [ CrossRef ]
• Di Asih, I. Maruddani, and Abdurakhman Abdurakhman. 2021. Delta-Normal Value at Risk Using Exponential Duration with Convexity for Measuring Government Bond Risk. DLSU Business and Economics Review 31: 72–80. [ Google Scholar ]
• Di Bucchianico, Alessandro. 2008. Coefficient of determination (R 2). Encyclopedia of Statistics in Quality, and Reliability 1. [ Google Scholar ] [ CrossRef ]
• DiMaggio, Paul J., and Walter W. Powell. 1983. And collective rationality in organizational fields. American Sociological Review 48: 147–60. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• El-Chaarani, Hani, Rebecca Abraham, and Yahya Skaf. 2022. The impact of corporate governance on the financial performance of the banking sector in the MENA (Middle Eastern and North African) region: An immunity test of banks for COVID-19. Journal of Risk and Financial Management 15: 82. [ Google Scholar ] [ CrossRef ]
• Finger, Maya, Ilanit Gavious, and Ronny Manos. 2018. Environmental risk management and financial performance in the banking industry: A cross-country comparison. Journal of International Financial Markets, Institutions, and Money 52: 240–61. [ Google Scholar ] [ CrossRef ]
• Finstad, Kraig. 2010. Response interpolation and scale sensitivity: Evidence against 5-point scales. Journal of Usability Studies 5: 104–10. [ Google Scholar ]
• Fite, D., and P. Pfleiderer. 1995. Should firms use derivatives to manage risk? In Risk Management: Problems and Solutions . New York: McGraw-Hill, pp. 139–69. [ Google Scholar ]
• Fornell, Claes, and David F. Larcker. 1981. Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research 18: 39–50. [ Google Scholar ] [ CrossRef ]
• George, Makori Ochogo. 2015. Effects of Credit Risk Management Practices on Profitability of Deposit Taking Sacco’s in Nairobi County. Master’s thesis, The Management University of Africa, Nairobi, Kenya. [ Google Scholar ]
• Gold, Andrew H., Arvind Malhotra, and Albert H. Segars. 2001. Knowledge management: An organizational capabilities perspective. Journal of Management Information Systems 18: 185–214. [ Google Scholar ] [ CrossRef ]
• Goss, Allen, and Gordon S. Roberts. 2011. The impact of corporate social responsibility on the cost of bank loans. Journal of Banking & Finance 35: 1794–810. [ Google Scholar ]
• Grody, Allan D., and Peter J. Hughes. 2016. Risk Accounting-Part 1: The risk data aggregation and risk reporting (BCBS 239) foundation of enterprise risk management (ERM) and risk governance. Journal of Risk Management in Financial Institutions 9: 130–46. [ Google Scholar ]
• Hair, Joe F., Christian M. Ringle, and Marko Sarstedt. 2011. PLS-SEM: Indeed a silver bullet. Journal of Marketing theory and Practice 19: 139–52. [ Google Scholar ] [ CrossRef ]
• Hair, Joe F., Jr., Matt C. Howard, and Christian Nitzl. 2020. Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of Business Research 109: 101–10. [ Google Scholar ] [ CrossRef ]
• Hanh, Phung Tran My, Nguyen Thi Hang, and Dinh Tran Ngoc Huy. 2021. Enhancing Roles of Banks and the Comparison of Market Risk and Risk Policy Implications in Group of Listed Vietnam Banks During 2 Stages: Pre and Post-Low Inflation Period. Revista Geintec-Gestao Inovacao e Tecnologias 11: 1723–35. [ Google Scholar ] [ CrossRef ]
• Hassan, M. Kabir, Ashraf Khan, and Andrea Paltrinieri. 2019. Liquidity risk, credit risk and stability in Islamic and conventional banks. Research in International Business and Finance 48: 17–31. [ Google Scholar ] [ CrossRef ]
• Henseler, Jörg, Christian M. Ringle, and Marko Sarstedt. 2015. A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science 43: 115–35. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Hu, Li-tze, and Peter M. Bentler. 1999. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal 6: 1–55. [ Google Scholar ] [ CrossRef ]
• Hummel, Katrin, Ute Laun, and Annette Krauss. 2021. Management of environmental and social risks and topics in the banking sector-An empirical investigation. The British Accounting Review 53: 100921. [ Google Scholar ] [ CrossRef ]
• Huy, Dinh Tran Ngoc. 2021. Banking sustainability for economic growth and socio-economic development–case in Vietnam. Turkish Journal of Computer and Mathematics Education 12: 2544–53. [ Google Scholar ]
• Huy, Dinh Tran Ngoc, Nguyen Ngoc Thach, Bui Minh Chuyen, Pham Thi Hong Nhung, Duc Thang Tran, and Tuan Anh Tran. 2021. Enhancing risk management culture for sustainable growth of Asia commercial bank-ACB in Vietnam under mixed effects of macro factors. Entrepreneurship and Sustainability Issues 8: 291. [ Google Scholar ]
• Ishtiaq, Muhammad. 2015. Risk Management in Banks: Determination of Practices and Relationship with Performance. Ph.D. thesis, University of Bedfordshire, Luton, UK. [ Google Scholar ]
• Jensen, Michael C., and William H. Meckling. 2019. Theory of the firm: Managerial behavior, agency costs and ownership structure. Journal of Financial Economics 3: 305–60. [ Google Scholar ] [ CrossRef ]
• Kargi, Hamisu Suleiman. 2011. Credit Risk and the Performance of Nigerian Banks . Zaria: Ahmadu Bello University. [ Google Scholar ]
• Karumba, Mary, and Martin Wafula. 2012. Collateral Lending: Are There Alternatives for the Kenyan Banking Industry? Nairobi: Kenya Bankers Association (KBA). [ Google Scholar ]
• Keramati, Abbas, Rose Taeb, Arad Mousavi Larijani, and Navid Mojir. 2012. A combinative model of behavioural and technical factors affecting ‘Mobile’-payment services adoption: An empirical study. The Service Industries Journal 32: 1489–504. [ Google Scholar ] [ CrossRef ]
• Khalid, Azam Abdelhakeem, Wala Abdelmunem Mohamed Hassan, Neimat Abdalla Ibrahim, Yousif Abdelbagi Abdalla, Ibrahim Elsiddig Ahmed, and Adel M. Sarea. 2021. The Impact of Credit Risk Management on the Financial Performance of Banking Sector in Sudan. Academy of Accounting and Financial Studies Journal 25: 1–11. [ Google Scholar ]
• Klein, Howard J., Michael J. Wesson, John R. Hollenbeck, Patrick M. Wright, and Richard P. DeShon. 2001. The assessment of goal commitment: A measurement model meta-analysis. Organizational Behavior and Human Decision Processes 85: 32–55. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Kline, Rex B. 2011. Principles and Practice of Structural Equation Modeling , 3rd ed. New York: Guilford Press. [ Google Scholar ]
• Kothari, Chakravanti Rajagopalachari. 2004. Research Methodology: Methods and Techniques . Delhi: New Age International. [ Google Scholar ]
• Kuznichenko, Yana, Serhiy Frolov, Fedir Zhuravka, Mykola Yefimov, and Volodymyr Fedchenko. 2018. Regulatory assessment of the bank market risk: International approaches and Ukrainian practice. Banks & Bank Systems 13: 73–84. [ Google Scholar ]
• Liu, Na, Xiaowei Jin, Chenglian Feng, Zijian Wang, Fengchang Wu, Andrew C. Johnson, Hongxia Xiao, Henner Hollert, and John P. Giesy. 2020. Ecological risk assessment of fifty pharmaceuticals and personal care products (PPCPs) in Chinese surface waters: A proposed multiple-level system. Environment International 136: 105454. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Martens, David, B. B. Baesens, and Tony Van Gestel. 2008. Decompositional rule extraction from support vector machines by active learning. IEEE Transactions on Knowledge and Data Engineering 21: 178–91. [ Google Scholar ] [ CrossRef ]
• McDonald, Roderick P. 1996. Path analysis with composite variables. Multivariate Behavioral Research 31: 239–70. [ Google Scholar ] [ CrossRef ]
• Mercylynne, Mumbi Wanjugu, and Job Omagwa. 2017. Credit risk management and financial performance of selected commercial banks in Kenya. Journal of Business and Management 19: 92–98. [ Google Scholar ]
• Misati, Roseline Nyakerario, and Anne Kamau. 2015. Local and International Dimensions to Credit Provision by Commercial Banks in Kenya . Nairobi: Kenya Bankers Association (KBA). [ Google Scholar ]
• Naili, Maryem, and Younes Lahrichi. 2022. The determinants of banks’ credit risk: Review of the literature and future research agenda. International Journal of Finance & Economics 27: 334–60. [ Google Scholar ]
• Ndero, Salome Watiri, Joshua Matanda Wepukhulu, and Jared Bitange Bogonko. 2019. Relationship between credit appraisal and loan performance by commercial banks in uasin gishu county, Kenya. European Journal of Economic and Financial Research , 3. [ Google Scholar ] [ CrossRef ]
• Nenty, H. Johnson. 2009. Writing a quantitative research thesis. International Journal of Educational Sciences 1: 19–32. [ Google Scholar ] [ CrossRef ]
• Nguyen, Quang Khai. 2020. Ownership structure and bank risk-taking in ASEAN countries: A quantile regression approach. Cogent Economics & Finance 8: 1809789. [ Google Scholar ]
• Nguyen, Quang Khai. 2022. Audit committee structure, institutional quality, and bank stability: Evidence from ASEAN countries. Finance Research Letters 46: 102369. [ Google Scholar ] [ CrossRef ]
• Nguyen, Quang Khai, and Van Cuong Dang. 2023. Does the country’s institutional quality enhance the role of risk governance in preventing bank risk? Applied Economics Letters 30: 850–53. [ Google Scholar ] [ CrossRef ]
• Nizam, Esma, Adam Ng, Ginanjar Dewandaru, Ruslan Nagayev, and Malik Abdulrahman Nkoba. 2019. The impact of social and environmental sustainability on financial performance: A global analysis of the banking sector. Journal of Multinational Financial Management 49: 35–53. [ Google Scholar ] [ CrossRef ]
• Noory, Siti Najihah, Shahida Shahimi, and Abd Ghafar Bin Ismail. 2021. A Systematic Literature Review on the Effects of Risk Management Practices on the Performance of Islamic Banking Institutions. Asian Journal of Accounting and Governance 16: 53–75. [ Google Scholar ]
• Olken, Frank, and Doron Rotem. 1995. Random sampling from databases: A survey. Statistics and Computing 5: 25–42. [ Google Scholar ] [ CrossRef ]
• Ongore, Vincent Okoth, and Gemechu Berhanu Kusa. 2013. Determinants of financial performance of commercial banks in Kenya. International Journal of Economics and Financial Issues 3: 237–52. [ Google Scholar ]
• Oudat, Mohammad Salem, and Basel J. A. Ali. 2021. The Underlying Effect of Risk Management On Banks’ Financial Performance: An Analytical Study on Commercial and Investment Banking in Bahrain. Ilkogretim Online 20: 404–14. [ Google Scholar ]
• Paudel, Gyanendra Prasad. 2022. Credit Risk Management in Nepalese Cooperative Societies . Kathmandu: Thuprai Solutions Pvt. Ltd. [ Google Scholar ]
• Pauline, Emmanuel, and Viswa Nadham. 2022. Effect of Credit Information Bureau and Appraisal Methods on performance of Commercial Banks in Mwanza Region. International Journal of Engineering, Business and Management 6: 24–38. [ Google Scholar ] [ CrossRef ]
• Phuong, Nguyen Thi Thanh, Dinh Tran Ngoc Huy, and Pham Van Tuan. 2020. The evaluation of impacts of a seven factor model on nvb stock price in commercial banking industry in vietnam-and roles of Discolosure of Accounting Policy In Risk Management. International Journal of Entrepreneurship 24: 1–13. [ Google Scholar ]
• Poudel, Ravi Prakash Sharma. 2012. The impact of credit risk management on financial performance of commercial banks in Nepal. International Journal of Arts, and Commerce 1: 9–15. [ Google Scholar ]
• Radelet, Steven, Jeffrey D. Sachs, Richard N. Cooper, and Barry P. Bosworth. 1998. The East Asian financial crisis: Diagnosis, remedies, prospects. Brookings Papers on Economic Activity 1998: 1–90. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Rahi, Samar. 2017. Research design and methods: A systematic review of research paradigms, sampling issues and instruments development. Journal of Economics & Management Sciences 6: 1–5. [ Google Scholar ]
• Rajendran, R. 2022. Impact of Basel accord on corporate governance in risk management. Specialusis Ugdymas 1: 1728–38. [ Google Scholar ]
• Rehman, Zia Ur, Noor Muhammad, Bilal Sarwar, and Muhammad Asif Raz. 2019. Impact of risk management strategies on the credit risk faced by commercial banks of Balochistan. Financial Innovation 5: 1–13. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Saleem, Adil, Judit Bárczi, and Judit Sági. 2021. COVID-19 and Islamic stock index: Evidence of market behavior and volatility persistence. Journal of Risk and Financial Management 14: 389. [ Google Scholar ] [ CrossRef ]
• Saleh, Isam, and Malik Abu Afifa. 2020. The effect of credit risk, liquidity risk and bank capital on bank profitability: Evidence from an emerging market. Cogent Economics & Finance 8: 1814509. [ Google Scholar ]
• Santomero, Anthony M. 1997. Commercial bank risk management: An analysis of the process. Journal of Financial Services Research 12: 83–115. [ Google Scholar ] [ CrossRef ]
• Sarfraz, Muddassar, Wang Qun, Li Hui, and Muhammad Ibrahim Abdullah. 2018. Environmental risk management strategies and the moderating role of corporate social responsibility in project financing decisions. Sustainability 10: 2771. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Scandura, Terri A., and Ethlyn A. Williams. 2000. Research methodology in management: Current practices, trends, and implications for future research. Academy of Management Journal 43: 1248–64. [ Google Scholar ] [ CrossRef ]
• Schmit, Joan T., and Kendall Roth. 1990. Cost effectiveness of risk management practices. Journal of Risk and Insurance 57: 455–70. [ Google Scholar ] [ CrossRef ]
• Scholtens, Bert. 2006. Finance as a driver of corporate social responsibility. Journal of Business Ethics 68: 19–33. [ Google Scholar ] [ CrossRef ]
• Scholtens, Bert, and Sophie van’t Klooster. 2019. Sustainability and bank risk. Palgrave Communications 5: 1–8. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Schroeck, Gerhard. 2002. Risk Management and Value Creation in Financial Institutions . New York: John Wiley & Sons, vol. 155. [ Google Scholar ]
• Shafiq, Afsheen, and Mohamed Nasr. 2010. Risk Management Practices Followed by the Commercial Banks in Pakistan. International Review of Business Research Papers 6: 308–25. [ Google Scholar ]
• Smith, Clifford W., and Rene M. Stulz. 1985. The determinants of firms’ hedging policies. Journal of Financial and Quantitative Analysis 20: 391–405. [ Google Scholar ] [ CrossRef ]
• Suyanto, Suyanto. 2021. The effect of bad credit and liquidity on bank performance in Indonesia. The Journal of Asian Finance, Economics and Business 8: 451–58. [ Google Scholar ]
• Taskinsoy, John. 2019. The Global Competitiveness Index: A Comparative Analysis between Turkey and G8 Nations. Available online: https://ssrn.com/abstract=3500542 (accessed on 8 December 2019).
• Thisika, L. M., and W. Muturi. 2017. Effects of Credit Risk Management on Loan Performance in Kenyan Commercial Banks. Journal of Economics, Commerce and Management 5: 486–96. [ Google Scholar ]
• Tolbert, Pamela S., and Lynne G. Zucker. 1983. Institutional sources of change in the formal structure of organizations: The diffusion of civil service reform, 1880–1935. Administrative Science Quarterly 28: 22–39. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Tsai, I-Chun. 2017. The source of global stock market risk: A viewpoint of economic policy uncertainty. Economic Modelling 60: 122–31. [ Google Scholar ] [ CrossRef ]
• Tufano, Peter. 1998. Agency costs of corporate risk management. Financial Management 27: 67–77. [ Google Scholar ] [ CrossRef ]
• Ugah, John. 2020. Financial risks management and bank profitability in Nigeria: Case of Access Bank of Nigeria Plc. International Journal of Research, and Innovation in Social Science , 183–90. [ Google Scholar ]
• Uzah, Cheta Kingsley, and Chukwudi Chinweuba Omire. 2021. Default and Credit Risk: The Implication on Capital Adequacy of Nigerian Commercial Banks . Port Harcourt: Book of Proceedings of the Second International Virtual Conference. [ Google Scholar ]
• Velliscig, Giulio, Josanco Floreani, and Maurizio Polato. 2022. Capital and asset quality implications for bank resilience and performance in the light of NPLs’ regulation: A focus on the Texas ratio. Journal of Banking Regulation , 1–23. [ Google Scholar ] [ CrossRef ]
• Wang, Chih-Wei, Chien-Chiang Lee, and Ming-Chien Chen. 2022. The effects of economic policy uncertainty and country governance on banks’ liquidity creation: International evidence. Pacific-Basin Finance Journal 71: 101708. [ Google Scholar ] [ CrossRef ]
• Weber, Elke U., Ann-Renee Blais, and Nancy E. Betz. 2002. A domain-specific risk-attitude scale: Measuring risk perceptions and risk behaviors. Journal of Behavioral Decision Making 15: 263–90. [ Google Scholar ] [ CrossRef ]
• Weber, Olaf, Marcus Fenchel, and Roland W. Scholz. 2008. Empirical analysis of the integration of environmental risks into the credit risk management process of European banks. Business Strategy and the Environment 17: 149–59. [ Google Scholar ] [ CrossRef ]
• Weber, Olaf, Roland W. Scholz, and Georg Michalik. 2010. Incorporating sustainability criteria into credit risk management. Business Strategy and the Environment 19: 39–50. [ Google Scholar ] [ CrossRef ]
• White, Piran C. L., Nancy Vaughan Jennings, Anna R. Renwick, and Nola H. L. Barker. 2005. Questionnaires in ecology: A review of past use and recommendations for best practice. Journal of Applied Ecology 42: 421–30. [ Google Scholar ] [ CrossRef ]
• Xi, Bin, Jiali Dai, and Yun Liu. 2022. Does environmental information disclosure affect the financial performance of commercial banks? Evidence from China. Environmental Science and Pollution Research 29: 65826–41. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Yang, Yubin, Xuejian Chu, Ruiqi Pang, Feng Liu, and Peifang Yang. 2021. Identifying and predicting the credit risk of small and medium-sized enterprises in sustainable supply chain finance: Evidence from China. Sustainability 13: 5714. [ Google Scholar ] [ CrossRef ]
• Yellen, Janet L. 2017. Inflation, uncertainty, and monetary policy. Business Economics 52: 194–207. [ Google Scholar ] [ CrossRef ]
• Zhou, Xiao Yan, Ben Caldecott, Andreas G. F. Hoepner, and Yao Wang. 2022. Bank green lending and credit risk: An empirical analysis of China’s Green Credit Policy. Business Strategy and the Environment 31: 1623–40. [ Google Scholar ] [ CrossRef ]

Click here to enlarge figure

S. No.	Items for Measuring Credit Risk Management	Reference
1	The credit risk strategy set by the board of directors is effectively transformed and communicated within the bank in the shape of policies and procedures by the top management	( )
2	The bank has an effective risk management framework (infrastructure, process, and policies) in place for managing credit risk
3	The bank has a credit risk rating framework across all types of credit activities
4	The bank monitors the quality of the credit portfolio on a day-to-day basis and takes remedial measures if and when any deterioration occurs
		Reference
1	The business incubator helps with improving entrepreneurial skills	( )
2	Business incubators provide an opportunity to create innovative business ideas
3	The business incubator helps with improving business skills
4	Business incubators play an important meditating role between skills development, access to finance, business networking, and entrepreneurship development
		Reference
1	The credit department always checks the character of the borrower during credit review	( )
2	The credit department always checks the collateral of the borrower during credit review
3	The credit department always checks at the capacity of the borrower during credit review
4	The credit department always checks the capital of the borrower during credit review
		Reference
1	The market risk strategy set by the board of directors is effectively transformed and communicated within the bank in the shape of policies and procedures by the top management	( ) and ( )
2	The bank has an effective risk management framework (infrastructure, process, and policies) in place for managing market risk
3	The bank’s overall market risk exposure is maintained at prudent levels consistent with the available capital
4	The bank adopts multiple risk measurement methodologies to capture market risk in various business activities
		Reference
1	The bank’s executive management regularly reviews the organization’s performance in managing its business risks	( ; )
2	The bank has highly effective continuous review/feedback on risk management strategies and performance
3	The bank’s risk management procedures and processes are documented and guide staff in managing risks
4	Overall, the level of risk management practices of the bank is considered to be excellent

Variable		Frequency	Percentage (%)
Sex
	Male	173	81.9%
	Female	38	18.1%
Age (years)
	21–30	22	10.4%
	31–40	89	42.2%
	41–50	70	33.1%
	51–60	30	14.2%
Managerial level
	Top management	92	43.6%
	Middle management	68	32.2%
	Lower management	31	14.6%
	Retired	20	9.4%
Education
	Bachelors	63	29.8%
	Masters	136	64.4%
	Ph.D.	12	5.6%
Experience (years)
	1–5	17	8.0%
	6–10	45	21.23%
	11–15	79	37.44%
	16–20	70	33.17%

Construct	Item	Loading	CA	CR	AVE	Inner VIF	f
Environmental Risk (ER)	ER1	0.821	0.872	0.913	0.724	2.231	0.121
	ER 2	0.844
	ER3	0.875
	ER4	0.861
Credit Appraisal Measurement (CAM)	CAM 1	0.730	0.824	0.884	0.657	1.805	0.255
	CAM 2	0.875
	CAM 3	0.857
	CAM 4	0.773
Market Risk Analysis (MRA)	MRA 1	0.775	0.841	0.894	0.679	1.875	0.08
	MRA 2	0.876
	MRA 3	0.863
	MRA4	0.777
Credit Risk Management (CRM)	CRM 1	0.865	0.830	0.889	0.671	2.224	0.021
	CRM 2	0.860
	CRM 3	0.890
	CRM 4	0.636
Performance of Commercial Banks (POCB)	POCB 1	0.883	0.740	0.845	0.618	1.760	0.102
	POCB 2	0.927
	POCB 3	0.895
	POCB 4	0.180

Construct	CAM	CRM	ER	MRA	POCB
CAM	0.891
CRM	0.715	0.819
ER	0.700	0.756	0.851
MRA	0.858	0.767	0.829	0.874
POCB	0.769	0.723	0.709	0.712	0.786

Construct	CAM	CRM	ER	MRA	POCB
CAM
CRM	0.820
ER	0.719	0.720
MRA	0.832	0.852	0.780
POCB	0.726	0.734	0.742	0.810

	R-Squared	Adjusted R-Squared
CRM	0.648	0.643
POCB	0.523	0.521

	Path Coefficient	SD	T Statistics	p Value
ER -> CRM	0.393	0.081	4.838	0.000
CAM -> CRM	0.234	0.09	2.608	0.009
MRA -> CRM	0.24	0.107	2.235	0.025
CRM ->POCB	0.723	0.039	18.542	0.000

Relationship	Path Coefficient	Standard Deviation	T Statistic	p Value
ER -> CRM ->POCB	0.284	0.056	5.094	0.000
CAM -> CRM ->POCB	0.169	0.067	2.531	0.011
MRA -> CRM ->POCB	0.173	0.081	2.145	0.032

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

Bhatt, T.K.; Ahmed, N.; Iqbal, M.B.; Ullah, M. Examining the Determinants of Credit Risk Management and Their Relationship with the Performance of Commercial Banks in Nepal. J. Risk Financial Manag. 2023 , 16 , 235. https://doi.org/10.3390/jrfm16040235

Bhatt TK, Ahmed N, Iqbal MB, Ullah M. Examining the Determinants of Credit Risk Management and Their Relationship with the Performance of Commercial Banks in Nepal. Journal of Risk and Financial Management . 2023; 16(4):235. https://doi.org/10.3390/jrfm16040235

Bhatt, Tribhuwan Kumar, Naveed Ahmed, Muhammad Babar Iqbal, and Mehfooz Ullah. 2023. "Examining the Determinants of Credit Risk Management and Their Relationship with the Performance of Commercial Banks in Nepal" Journal of Risk and Financial Management 16, no. 4: 235. https://doi.org/10.3390/jrfm16040235

Article Metrics

Article access statistics, further information, mdpi initiatives, follow mdpi.

Subscribe to receive issue release notifications and newsletters from MDPI journals

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

Asian Journal of Accounting Research

ISSN : 2459-9700

Article publication date: 14 October 2021

Issue publication date: 27 May 2022

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

Design/methodology/approach

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

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

Practical implications

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

Originality/value

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

• South Asian countries

Credit risk

Bank-specific factors.

• Generalized method of moment

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

Emerald Publishing Limited

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

Published in Asian Journal of Accounting Research . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

Introduction

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

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

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

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

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

Literature review

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

Credit risk and financial performance

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

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

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

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

Bank-specific variables and financial performance

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

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

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

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

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

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

Data and methodology

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

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

Operational definition

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

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

Nonperforming loans

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

Methodology

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

Model specification

The regression model is as follows:.

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

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

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

Testing for autocorrelation

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

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

Testing for endogeneity

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

Findings and discussion

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

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

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

Regression results and discussion

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

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

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

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

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

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

NPLs-continent wise

Summary of explanatory variables and dependent variables

		Name of variable	Symbol	Measurement
Dependent variable	Financial performance	Return on asset	ROA
		Return on equity	ROE
Independent variable	Credit risk	Nonperforming loans	NPLs
		Capital adequacy ratio	CAR
	Bank-specific factors	Cost-efficiency ratio	CER
		Average lending rate	ALR
		Liquidity ratio	LR
Control variables		Bank size	BZ	Log (total assets)
		Inflation	Inflation	Annual inflation rate declared by word bank
		Age	Age	Age of commercial banks

Results for autocorrelation for South Asia countries

Wooldridge test for autocorrelation in panel data in developing countries
Model ROA	Model ROE
= 111.092	= 7.447
Prob >   = 0.0000	Prob >   = 0.0138

Descriptive statistics

Variables	Mean	Maximum	Minimum	Std. dev.	Observations
ROA	0.986	10.408	−6.234	1.905	190
ROE	7.964	100.158	−268.759	39.175	190
NPL	7.206	64.058	0.271	9.659	190
CAR	13.885	39.130	1.050	4.183	190
CER	27.920	68.696	13.050	9.808	190
ALR	8.723	14.701	5.542	1.615	190
LR	68.016	107.179	25.027	14.177	190
YES	3.899	5.008	2.318	0.589	190
YES	49.131	111	5	34.171	190
YES	9.449	20.92	2.540	3.891	190

Correlation figures

Variable	ROA	ROE	NPL	CAR	CER	LR	ALR	SIZE	AGE	INF
ROA	1
ROE	0.757	1
NPL	−0.225	−0.378	1
CAR	0.184	0.156	−0.284	1
CER	−0.170	−0.195	0.058	0.407	1
LR	0.026	0.010	−0.305	−0.121	−0.402	1
ALR	0.140	0.020	0.143	0.091	0.133	−0.237	1
YES	0.298	0.305	−0.213	−0.025	−0.335	0.457	−0.441	1
YES	−0.007	0.019	−0.182	−0.063	−0.310	0.213	−0.197	0.099	1
YES	−0.013	−0.171	0.163	−0.019	0.024	0.152	0.520	−0.162	−0.135	1

ROA model (pooled regression and fixed effect GMM result)

Pooled regression					Generalized method of moments
Variable	Coefficient	Std. Error	-Statistic	Prob.	Coefficient	Std. Error	-Statistic	Prob.
C	−5.449	1.754	−3.105	0.002*	−7.098	1.959	−3.621	0.000
NPL	−0.034	0.014	−2.434	0.015**	−0.032	0.015	−2.088	0.038**
CAR	0.080	0.033	2.417	0.016**	0.085	0.036	2.329	0.021**
CER	−0.043	0.015	−2.824	0.005*	−0.048	0.016	−2.972	0.003*
ALR	0.409	0.099	4.131	0.000*	0.492	0.112	4.392	0.000*
LR	−0.025	0.011	−2.296	0.022**	−0.027	0.013	−2.125	0.035**
YES	1.371	0.254	5.383	0.000*	1.649	0.292	5.645	0.000*
YES	−0.002	0.003	−0.588	0.557	−0.000	0.004	−0.026	0.978
YES	−0.031	0.039	−0.805	0.421	−0.032	0.061	−0.525	0.599
	0.282				0.372
Adjusted	0.250				0.358
S.E. of regression	1.650				1.672
Durbin–Watson stat	1.834				1.980
Hause test ( )					50.960
-value( )					0.000

*Indicates significance at 1% level, ** Indicates significance at 5% level, *** Indicates significance at the 10% level

Pooled regression					Generalized method of moments
Variable	Coefficient	Std. Error	-Statistic	Prob.	Coefficient	Std. Error	-Statistic	Prob.
C	−16.229	39.885	−0.407	0.685	−26.740	44.139	−0.606	0.546
NPL	−1.418	0.327	−4.337	0.000*	−1.379	0.354	−3.893	0.000*
CAR	1.261	0.713	1.769	0.079***	1.315	0.774	1.699	0.091***
CER	−1.035	0.350	−2.953	0.004*	−1.032	0.375	−2.754	0.007*
LR	−0.464	0.232	−2.000	0.047**	−0.463	0.262	−1.768	0.079***
ALR	3.981	2.037	1.954	0.052***	4.331	2.238	1.936	0.055***
YES	16.309	6.355	2.566	0.011**	18.481	7.001	2.640	0.009*
YES	−0.113	0.105	−1.082	0.281	−0.097	0.118	−0.824	0.411
YES	−1.623	0.734	−2.211	0.028**	−1.867	0.857	−2.178	0.031**
	0.234				0.265
Adjusted	0.231				0.249
S.E. of regression	30.442				29.378
Durbin–Watson stat	1.340				1.511
Hause test ( )					18.183
-value ( )					0.000

*Indicates significance at 1% level, ** Indicates significance at 5% level, *** Indicates significance at the 10% level

South Asian countries (ROA)VariableFixedRandomProbNPLs−0.066068−0.0346340.0046ALR0.2223110.2159840.9208CAR0.0016130.0681080.0002CER−0.027827−0.0307490.8033LR0.014278−0.0010340.0021SG0.0118740.0111500.7116SIZE−4.4466780.3399180.0000INFLATION−0.055072−0.0533490.8938AGE0.273576−0.0026880.0000

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

Accornero , M. , Cascarino , G. , Felici , R. , Parlapiano , F. and Sorrentino , A.M. ( 2018 ), “ Credit risk in banks' exposures to non-financial firms ”, European Financial Management , Vol. 24 No. 5 , pp. 775 - 791 .

Adebayo , M. , Adeyanju , D. and Olabode , S. ( 2011 ), “ Liquidity management and commercial banks' profitability in Nigeria ”, Research Journal of Finance and Accounting , Vol. 2 No. 8 .

Akhtar , M.F. , Ali , K. and Sadaqat , S. ( 2011 ), “ Factors influencing the profitability of Islamic banks of Pakistan ”, International Research Journal of Finance and Economics , Vol. 66 , pp. 125 - 132 .

Aspal , P.K. , Dhawan , S. and Nazneen , A. ( 2019 ), “ Significance of bank specific and macroeconomic determinants on performance of Indian private sector banks ”, International Journal of Economics and Financial Issues , Vol. 9 No. 2 , p. 168 .

Belas , J. , Smrcka , L. , Gavurova , B. and Dvorsky , J. ( 2018 ), “ The impact of social and economic factors in the credit risk management of SME ”, Technological and Economic Development of Economy , Vol. 24 No. 3 , pp. 1215 - 1230 .

Berger , A.N. and DeYoung , R. ( 1997 ), “ Problem loans and cost efficiency in commercial banks ”, Journal of Banking and Finance , Vol. 21 , pp. 849 - 870 .

Blum , J. and Hellwig , M. ( 1995 ), “ The macroeconomic implications of capital adequacy requirements for banks ”, European Economic Review , pp. 739 - 749 .

Brooks , C. ( 2014 ), Introductory Econometrics for Finance , Cambridge University Press .

Chimkono , E.E. , Muturi , W. and Njeru , A. ( 2016 ), “ Effect of non-performing loans and other factors on performance of commercial banks in Malawi ”, International Journal of Economics, Commerce and Management , Vol. IV , pp. 1 - 15 .

Ekinci , R. and Poyraz , G. ( 2019 ), “ The effect of credit risk on financial performance of deposit banks in Turkey ”, Procedia Computer Science , Vol. 158 , pp. 979 - 987 .

Francis , B.B. , Hasan , I. , Song , L. and Yeung , B. ( 2015 ), “ What determines bank-specific variations in bank stock returns? Global evidence ”, Journal of Financial Intermediation , Vol. 24 No. 3 , pp. 312 - 324 .

Ghenimi , A. , Chaibi , H. and Omri , M.A.B. ( 2017 ), “ The effects of liquidity risk and credit risk on bank stability: evidence from the MENA region ”, Borsa Istanbul Review , Vol. 17 No. 4 , pp. 238 - 248 .

Ghosh , A. ( 2015 ), “ Banking-industry specific and regional economic determinants of non-performing loans: evidence from US states ”, Journal of Financial Stability , Vol. 20 , pp. 93 - 104 .

Hamza , S.M. ( 2017 ), “ Impact of credit risk management on banks performance: a case study in Pakistan banks ”, European Journal of Business and Management , Vol. 9 No. 1 , pp. 57 - 64 .

Hasanov , F. , Bayramli , N. and Al-Musehel , N. ( 2018 ), “ Bank-specific and macroeconomic determinants of bank profitability: evidence from an oil-dependent economy ”, International Journal of Financial Studies , Vol. 6 No. 3 , p. 78 .

Hassan , M.K. , Khan , A. and Paltrinieri , A. ( 2019 ), “ Liquidity risk, credit risk and stability in Islamic and conventional banks ”, Research in International Business and Finance , Vol. 48 , pp. 17 - 31 .

Khan , M.A. , Siddique , A. and Sarwar , Z. ( 2020 ), “ Determinants of non-performing loans in the banking sector in developing state ”, Asian Journal of Accounting Research .

Kusi , B.A. , Agbloyor , E.K. , Ansah-Adu , K. and Gyeke-Dako , A. ( 2017 ), “ Bank credit risk and credit information sharing in Africa: does credit information sharing institutions and context matter? ”, Research in International Business and Finance , Vol. 42 , pp. 1123 - 1136 .

Lassoued , M. ( 2018 ), “ Comparative study on credit risk in Islamic banking institutions: the case of Malaysia ”, The Quarterly Review of Economics and Finance , Vol. 70 , pp. 267 - 278 .

Louzis , D.P. , Vouldis , A.T. and Metaxas , V.L. ( 2012 ), “ Macroeconomic and bank-specific determinants of non-performing loans in Greece: a comparative study of mortgage, business and consumer loan portfolios ”, Journal of Banking and Finance , Vol. 36 No. 4 , pp. 1012 - 1027 .

Masood , O. and Ashraf , M. ( 2012 ), “ Bank-specific and macroeconomic profitability determinants of Islamic banks: the case of different countries ”, Qualitative Research in Financial Markets , Vol. 4 Nos 2/3 , pp. 255 - 268 .

Ofori-Abebrese , G. , Pickson , R.B. and Opare , E. ( 2016 ), “ The effect of bank specific factors on loan performance of HFC bank in Ghana ”, International Journal of Economics and Finance , Vol. 8 No. 7 , p. 185 .

Oluwafemi , A.S. , Adebisi , A.N.S. , Simeon , O. and Olawale , O. ( 2013 ), “ Risk management and financial performance of banks in Nigeria ”, Journal of Business and Management , Vol. 14 No. 6 , pp. 52 - 56 .

Rachman , R.A. , Kadarusman , Y.B. , Anggriono , K. and Setiadi , R. ( 2018 ), “ bank-specific factors affecting non-performing loans in developing countries: case study of Indonesia ”, The Journal of Asian Finance, Economics and Business (JAFEB) , Vol. 5 No. 2 , pp. 35 - 42 .

Raphael , G. ( 2013 ), “ Bank-specific, industry-specific and macroeconomic determinants of bank efficiency in Tanzania: a two stage analysis ”, European Journal of Business and Management , Vol. 5 No. 2 , pp. 142 - 154 .

Sekaran , U. ( 2006 ), Research Methods for Business: A Skill Building Approach , John Wiley & Sons .

Siddique , A. , Masood , O. , Javaria , K. and Huy , D.T.N. ( 2020 ), “ A comparative study of performance of commercial banks in ASIAN developing and developed countries ”, Insights Into Regional Development .

Corresponding author

Related articles, all feedback is valuable.

Please share your general feedback

Report an issue or find answers to frequently asked questions

Contact Customer Support

Explainable Machine Learning in Credit Risk Management

• Open access
• Published: 25 September 2020
• Volume 57 , pages 203–216, ( 2021 )

Cite this article

You have full access to this open access article

• Niklas Bussmann 1 ,
• Paolo Giudici   ORCID: orcid.org/0000-0002-4198-0127 1 ,
• Dimitri Marinelli 2 &
• Jochen Papenbrock 3  

40k Accesses

180 Citations

38 Altmetric

Explore all metrics

The paper proposes an explainable Artificial Intelligence model that can be used in credit risk management and, in particular, in measuring the risks that arise when credit is borrowed employing peer to peer lending platforms. The model applies correlation networks to Shapley values so that Artificial Intelligence predictions are grouped according to the similarity in the underlying explanations. The empirical analysis of 15,000 small and medium companies asking for credit reveals that both risky and not risky borrowers can be grouped according to a set of similar financial characteristics, which can be employed to explain their credit score and, therefore, to predict their future behaviour.

Similar content being viewed by others

A Systematic Review of Machine Learning and Explainable Artificial Intelligence (XAI) in Credit Risk Modelling

Machine learning-driven credit risk: a systemic review

Can we trust machine learning to predict the credit risk of small businesses?

Explore related subjects.

• Artificial Intelligence

Avoid common mistakes on your manuscript.

1 Introduction

Black box Artificial Intelligence (AI) is not suitable in regulated financial services. To overcome this problem, Explainable AI models, which provide details or reasons to make the functioning of AI clear or easy to understand, are necessary.

To develop such models, we first need to understand what “Explainable” means. Recently, some important insitutional definitions have been provided. For example, Bracke et al. ( 2019 ) states that “Explanations can answer different kinds of questions about a model's operation depending on the stakeholder they are addressed to and Croxson et al. ( 2019 )” ‘interpretability’ will be the focus will be the focus—generally taken to mean that an interested stakeholder can comprehend the main drivers of a model-driven decision".

Explainability means that an interested stakeholder can comprehend the main drivers of a model-driven decision; FSB ( 2017 ) suggests that “lack of interpretability and auditability of AI and Machine Learning (ML) methods could become a macro-level risk”; Croxson et al. ( 2019 ) establishes that “in some cases, the law itself may dictate a degree of explainability.”

The European GDPR EU ( 2016 ) regulation states that “the existence of automated decision-making should carry meaningful information about the logic involved, as well as the significance and the envisaged consequences of such processing for the data subject.” Under the GDPR regulation, the data subject is therefore, under certain circumstances, entitled to receive meaningful information about the logic of automated decision-making.

Finally, the European Commission High-Level Expert Group on AI presented the Ethics Guidelines for Trustworthy Artificial Intelligence in April 2019. Such guidelines put forward a set of seven key requirements that AI systems should meet in order to be deemed trustworthy. Among them three relate to the concept of “eXplainable Artificial Intelligence (XAI)” , and are the following.

Human agency and oversight: decisions must be informed, and there must be a human-in-the-loop oversight.

Transparency: AI systems and their decisions should be explained in a manner adapted to the concerned stakeholder. Humans need to be aware that they are interacting with an AI system.

Accountability: AI systems should develop mechanisms for responsibility and accountability, auditability, assessment of algorithms, data and design processes.

Following the need to explain AI models, stated by legislators and regulators of different countries, many established and startup companies have started to embrace Explainable AI models. In addition, more and more people are searching information about what “Explainable Artificial Intelligence” means.

In this respect, Fig.  1 represents the evolution of Google searches for explainable AI related terms.

From a mathematical viewpoint, it is well known that “simple” statistical learning models, such as linear and logistic regression models, provide a high interpretability but, possibly, a limited predictive accuracy. On the other hand, “complex” machine learning models, such as neural networks and tree models, provide a high predictive accuracy at the expense of a limited interpretability.

To solve this trade-off, we propose to boost machine learning models, that are highly accurate, with a novel methodology, that can explain their predictive output. Our proposed methodology acts in the post processing phase of the analysis, rather than in the preprocessing part. It is agnostic (technologically neutral) as it is applied to the predictive output, regardless of which model generated it: a linear regression, a classification tree or a neural network model.

The machine learning procedure proposed in the paper processes the outcomes of any other arbitrary machine learning model. It provides more insight, control and transparency to a trained, potentially black box machine learning model. It utilises a model-agnostic method aiming at identifying the decision-making criteria of an AI system in the form of variable importance (individual input variable contributions).

A key concept of our model is the Shapley value decomposition of a model, a pay-off concept from cooperative game theory. To the best of our knowledge this is the only explainable AI approach rooted in an economic foundation. It offers a breakdown of variable contributions so that every data point (e.g. a credit or loan customer in a portfolio) is not only represented by input features (the input of the machine learning model) but also by variable contributions to the prediction of the trained machine learning model.

More precisely, our proposed methodology is based on the combination of network analysis with Shapley values [see Lundberg and Lee ( 2017 ), Joseph ( 2019 ), and references therein]. Shapley values were originally introduced by Shapley ( 1953 ) as a solution concept in cooperative game theory. They correspond to the average of the marginal contributions of the players associated with all their possible orders. The advantage of Shapley values, over alternative XAI models, is that they can be exploited to measure the contribution of each explanatory variable for each point prediction of a machine learning model, regardless of the underlying model itself [see, e.g. Lundberg and Lee ( 2017 )]. In other words, Shapley based XAI models combine generality of application (they are model agnostic) with the personalisation of their results (they can explain any single point prediction).

Our original contribution is to improve Shapley values, improving the interpretation of the predictive output of a machine learning model by means of correlation network models. To exemplify our proposal, we consider one area of the financial industry in which Artificial Intelligence methods are increasingly being applied: credit risk management [see for instance the review by Giudici ( 2018 )].

Correlation networks, also known as similarity networks, have been introduced by Mantegna and Stanley ( 1999 ) to show how time series of asset prices can be clustered in groups on the basis of their correlation matrix. Correlation patterns between companies can similarly be extracted from cross-sectional features, based on balance sheet data, and they can be used in credit risk modelling. To account for such similarities we can rely on centrality measures, following Giudici et al. ( 2019 ) , who have shown that the inclusion of centrality measures in credit scoring models does improve their predictive utility. Here we propose a different use of similarity networks. Instead of applying network models in a pre-processing phase, as in Giudici et al. ( 2019 ) , who extract from them additional features to be included in a statistical learning model, we use them in a post-processing phase, to interpret the predictive output from a highly performing machine learning model. In this way we achieve both predictive accuracy and explainability.

We apply our proposed method to predict the credit risk of a large sample of small and medium enterprises. The obtained empirical evidence shows that, while improving the predictive accuracy with respect to a standard logistic regression model, we improve, the interpretability (explainability) of the results.

The rest of the paper is organized as follows: Sect.  2 introduces the proposed methodology. Section  3 shows the results of the analysis in the credit risk context. Section  4 concludes and presents possible future research developments.

2 Methodology

2.1 statistical learning of credit risk.

Credit risk models are usually employed to estimate the expected financial loss that a credit institution (such as a bank or a peer-to-peer lender) suffers, if a borrower defaults to pay back a loan. The most important component of a credit risk model is the probability of default, which is usually estimated statistically employing credit scoring models.

Borrowers could be individuals, companies, or other credit institutions. Here we focus, without loss of generality, on small and medium enterprises, whose financial data are publicly available in the form of yearly balance sheets.

For each company, n , define a response variable \(Y_{n}\) to indicate whether it has defaulted on its loans or not, i.e. \(Y_{n}=1\) if company defaults, \(Y_{n}=0\) otherwise. And let \(X_{n}\) indicate a vector of explanatory variables. Credit scoring models assume that the response variable \(Y_{n}\) may be affected (“caused”) by the explanatory variables \(X_{n}\) .

The most commonly employed model of credit scoring is the logistic regression model. It assumes that

where \(p_{n}\) is the probability of default for company n ; \({\mathbf {x}}_{n}=(x_{i,1},\ldots ,x_{i,J})\) is a J -dimensional vector containing the values that the J explanatory variables assume for company n ; the parameter \(\alpha \) represents an intercept; \(\beta _{j}\) is the j th regression coefficient.

Once the parameters \(\alpha \) and \(\beta _{j}\) are estimated using the available data, It the probability of default can be estimated, inverting the logistic regression model, from:

2.2 Machine Learning of Credit Risk

Alternatively, credit risk can be measured with Machine Learning (ML) models, able to extract non-linear relations among the financial information contained in the balance sheets. In a standard data science life cycle, models are chosen to optimise the predictive accuracy. In highly regulated sectors, like finance or medicine, models should be chosen balancing accuracy with explainability (Murdoch et al. 2019 ). We improve the choice selecting models based on their predictive accuracy, and employing a posteriori an algorithm that achieves explanability. This does not limit the choice of the best performing models.

To exemplify our approach we consider, without loss of generality, the Extreme Gradient Boost model, one of the most popular and fast machine learning algorithms [see e.g. Chen and Guestrin ( 2016 )].

Extreme Gradient Boosting (XGBoost) is a supervised model based on the combination of tree models with Gradient Boosting. Gradient Boosting is an optimisation technique able to support different learning tasks, such as classification, ranking and prediction. A tree model is a supervised classification model that searches for the partition of the explanatory variables that best classify a response (supervisor) variable. Extreme Gradient Boosting improves tree models strengthening their classification performance, as shown by Chen and Guestrin ( 2016 ). The same authors also show that XGBoost is faster than tree model algorithms.

In practice, a tree classification algorithm is applied successively to “training” samples of the data set. In each iteration, a sample of observations is drawn from the available data, using sampling weights which change over time, weighting more the observations with the worst fit. Once a sequence of trees is fit, and classifications made, a weighted majority vote is taken. For a more detailed description of the algorithm see, for instance (Friedman et al. 2000 ).

2.3 Learning Model Comparison

Once a default probability estimation model is chosen, it should be measured in terms of predictive accuracy, and compared with other models, so to select the best one. The most common approach to measure predictive accuracy of credit scoring models is to randomly split the available data in two parts: a “train” and a “test” set; build the model using data in the train set, and compare the predictions the model obtains on the test set, \(\hat{Y_n}\) , with the actual values of \(Y_n\) .

To obtain \(\hat{Y_n}\) the estimated default probability is rounded into a “default” or “non default”, depending on whether a threshold is passed or not. For a given threshold T , one can then count the frequency of the four possible outputs, namely: False Positives (FP): companies predicted to default, that do not; True Positives (TP): companies predicted to default, which do; False Negatives (FN): companies predicted not to default, which do; True Negatives (TN): companies predicted not to default, which do not.

The misclassification rate of a model can be computed as:

and it characterizes the proportion of wrong predictions among the total number of cases.

The misclassification rate depends on the chosen threshold and it is not, therefore, a generally agreed measure of predictive accuracy. A common practice is to use the Receiver Operating Characteristics (ROC) curve, which plots the false positive rate (FPR) on the Y axis against the true positive rate (TPR) on the X axis, for a range of threshold values (usually percentile values). FPR and TPR are then calculated as follows:

The ideal ROC curve coincides with the Y axis, a situation which cannot be realistically achieved. The best model will be the one closest to it. The ROC curve is usually summarised with the Area Under the ROC curve value (AUROC), a number between 0 and 1. The higher the AUROC, the better the model.

2.4 Explaining Model Predictions

We now explain how to exploit the information contained in the explanatory variables to localise and cluster the position of each individual (company) in the sample. This information, coupled with the predicted default probabilities, allows a very insightful explanation of the determinant of each individual’s creditworthiness. In our specific context, information on the explanatory variables is derived from the financial statements of borrowing companies, collected in a vector \({\mathbf {x}}_{n}\) , representing the financial composition of the balance sheet of institution n .

We propose to calculate the Shapley value associated with each company. In this way we provide an agnostic tool that can interpret in a technologically neutral way the output from a highly accurate machine learning model. As suggested in Joseph ( 2019 ), the Shapley values of a model can be used as a tool to transfer predictive inferences into a linear space, opening a wide possibility of applying to them a variety of multivariate statistical methods.

We develop our Shapley approach using the SHAP Lundberg and Lee ( 2017 ) computational framework, which allows to estimate Shapley values expressing predictions as linear combinations of binary variables that describe whether each single variable is included or not in the model.

More formally, the explanation model \(g(x')\) for the prediction f ( x ) is constructed by an additive feature attribution method, which decomposes the prediction into a linear function of the binary variables \(z' \in \{0,1\}^M\) and the quantities \(\phi _i \in {\mathbb {R}}\) :

In other terms, \(g'(z')\approx f(h_x (z'))\) is a local approximation of the predictions where the local function \(h_x (x')=x\) maps the simplified variables \(x'\) into x , \(z'\approx x\) and M is the number of the selected input variables.

Indeed, Lundberg and Lee ( 2017 ) prove that the only additive feature attribution method that satisfies the properties of local accuracy , missingness and consistency is obtained attributing to each feature \(x'_i\) an effect \(\phi _i\) called Shapley value, defined as

where f is the trained model, x the vector of inputs (features), \(x'\) the vector of the M selected input features. The quantity \(f_x(z') - f_x(z' {\setminus } i) \) is the contribution of a variable i and expresses, for each single prediction, the deviation of Shapley values from their mean.

In other words, a Shapley value represents a unique quantity able to construct an explanatory model that locally linearly approximate the original model, for a specific input x ,( local accuracy ). With the property that, whenever a feature is locally zero, the Shapley value is zero ( missingness ) and if in a second model the contribution of a feature is higher, so will be its Shapley value ( consistency ).

Once Shapley values are calculated, we propose to employ similarity networks, defining a metric that provides the relative distance between companies by applying the Euclidean distance between each pair \(({\mathbf {x}}_{i},{\mathbf {x}}_{j})\) of company predicted vectors, as in Giudici et al. ( 2019 ).

We then derive the Minimal Spanning Tree (MST) representation of the companies, employing the correlation network method suggested by Mantegna and Stanley ( 1999 ). The MST is a tree without cycles of a complex network, that joins pairs of vertices with the minimum total “distance”.

The choice is motivated by the consideration that, to represent all pairwise correlations between N companies in a graph, we need \(N*(N-1)/2\) edges, a number that quickly grows, making the corresponding graph not understandable. The Minimal Spanning Tree simplifies the graph into a tree of \(N-1\) edges, which takes \(N-1\) steps to be completed. At each step, it joins the two companies that are closest, in terms of the Euclidean distance between the corresponding explanatory variables.

In our Shapley value context, the similarity of variable contributions is expressed as a symmetric matrix of dimension n × n , where n Is the number of data points in the (train) data set. Each entry of the matrix measures how similar or distant a pair of data points is in terms of variable contributions. The MST representation associates to each point its closest neighbour. To generate the MST we have used the EMST Dual-Tree Boruvka algorithm, and its implementation in the R package “emstreeR”.

The same matrix can also be used, in a second step, for a further merging of the nodes, through cluster analysis. This extra step can reveal segmentations of data points with very similar variable contributions, corresponding to similar credit scoring decision making.

3 Application

We test our proposed model to data supplied by European External Credit Assessment Institution (ECAI) that specializes in credit scoring for P2P platforms focused on SME commercial lending. The data is described by Giudici et al. ( 2019 ) to which we refer for further details. In summary, the analysis relies on a dataset composed of official financial information (balance-sheet variables) on 15,045 SMEs, mostly based in Southern Europe, for the year 2015. The information about the status (0 = active, 1 = defaulted) of each company one year later (2016) is also provided. The proportion of defaulted companies within this dataset is 10.9%.

Using this data, Giudici et al. ( 2019 ) have constructed logistic regression scoring models that aim at estimating the probability of default of each company, using the available financial data from the balance sheets and, in addition, network centrality measures that are obtained from similarity networks.

Here we aim to improve the predictive performance of the model and, for this purpose, we run an XGBoost tree algorithm [see e.g. Chen and Guestrin ( 2016 )]. To explain the results from the model, typically highly predictive, we employ similarity network models, in a post-processing step. In particular, we employ the cluster dendrogram representation that corresponds to the application of the Minimum Spanning Tree algorithm.

3.2 Results

We first split the data in a training set (80%) and a test set (20%), using random sampling without replacement.

We then estimate the XGBoost model on the training set, apply the obtained model to the test set and compare it with the best logistic regression model. The ROC curves of the two models are contained in Fig.  1 below.

Receiver Operating Characteristic (ROC) curves for the logistic credit risk model and for the XGBoost model. In blue, we show the results related to the logistic models while in red we show the results related to the XGBoost model

From Fig.  1 note that the XGBoost clearly improves predictive accuracy. Indeed the comparison of the Area Under the ROC curve (AUROC) for the two models indicate an increase from 0.81 (best logistic regression model) to 0.93 (best XGBoost model).

We then calculate the Shapley value explanations of the companies in the test set, using the values of their explanatory variables. In particular, we use TreeSHAP method (Lundberg et al. 2020 ) [see e.g. Murdoch et al. ( 2019 ); Molnar 2019 )] in combination with XGBoost. The Minimal Spanning Tree (a single linkage cluster) is used to simplify and interpret the structure present among Shapley values. We can also "colour" the MST graph in terms of the associated response variables values: default, not default.

Figures  2 and  3 present the MST representation. While in Fig.  3 company nodes are colored according to the cluster to which they belong, in Fig.  4 they are colored according to their status: not defaulted (grey); defaulted (red).

Minimal Spanning Tree representation of the borrowing companies. Companies are colored according to their cluster of belonging

In Fig.  2 , nodes are colored according to the cluster in which they are classified. The figure shows that clusters are quite scattered along the correlation network.

To construct the colored communities in Fig.  2 , we used the algorithm implemented in the R package “igraph” that directly optimizes a modularity score. The algorithm is very efficient and easily scales to very large networks (Clauset et al. 2004 ).

In Fig.  3 , nodes are colored in a simpler binary way: whether the corresponding company has defaulted or not.

Minimal Spanning Tree representation of the borrowing companies. Clustering has been performed using the standardized Euclidean distance between institutions. Companies are colored according to their default status: red = defaulted; grey = not defaulted

From Fig.  3 note that default nodes appear grouped together in the MST representation, particularly along the bottom left branch. In general, defaulted institutions occupy precise portion of the network, usually to the leafs of the tree, and form clusters. This suggests that those companies form communities, characterised by similar predictor variables’ importances. It also suggests that not defaulted companies that are close to default ones have a high risk of becoming defaulted as well, being the importance of their predictor variables very similar to those of the defaulted companies.

To better explain the explainability of our results, in Fig.  4 we provide the interpretation of the estimated credit scoring of four companies: two that actually defaulted and two that did not.

Contribution of each explanatory variable to the Shapley’s decomposition of four predicted default probabilities, for two defaulted and two non defaulted companies. The more red the color the higher the negative importance, and the more blue the color the higher the positive importance

Figure  4 clearly shows the advantage of our explainable model. It can indicate which variables contribute more to the prediction of default. Not only in general, as is typically done by statistical and machine learning models, but differently and specifically for each company in the test set. Indeed, Fig.  4 clearly shows how the explanations are different (“personalised”) for each of the four considered companies.

The most important variables, for the two non defaulted companies (left boxes) regard: profits before taxes plus interests paid, and earnings before income tax and depreciation (EBITDA), which are common to both; trade receivables, for company 1; total assets, for company 2.

Economically, a high proficiency decreases the probability of default, for both companies; whereas a high stock of outstanding invoices, not yet paid, or a large stock of assets, helps reducing the same probability.

On the other hand, Fig.  4 shows that the most important variables, for the two defaulted companies (right boxes) concern: total assets, for both companies; shareholders funds plus non current liabilities, for company 3; profits before taxes plus interests paid, for company 4.

In other words, lower total assets coupled, in one case, with limited shareholder funds and, in the other, with low proficiency, increase the probability of default of these two companies.

The above results are consistent with previous analysis of the same data: both Giudici et al. ( 2019 ) select, as most important variables in several models, the return on equity, related to both EBITDA and profit before taxes plus interests paid; the leverage, related to total assets and shareholders’ funds; and the solvency ratio, related to trade payables.

We remark that Fig.  4 contains a “local” explanation of the predictive power of the explanatory variables, and it is the most important contribution of Shapley value theory. If we average Shapley values across all observations we get an “overall” or “global” explanation, similar to what already available in the statistical and machine learning literature. Figure  5 below provides the global explanation in our context: the ten most important explanatory variables, over the whole sample.

Mean contribution of each explanatory variable to the Shapley’s decomposition. The more red the color the higher the negative importance, and the more blue the color the higher the positive importance

From Fig.  5 note that total assets to total liabilities (the leverage) is the most important variable, followed by the EBITDA, along with profit before taxes plus interest paid, measures of operational efficiency; and by trade receivables, related to solvency, in line with the previous comments.

4 Conclusions and Future Research

The need to leverage the high predictive accuracy brought by sophisticated machine learning models, making them interpretable, has motivated us to introduce an agnostic, post-processing methodology, based on correlation network models. The model can explain, from a substantial viewpoint, any single prediction in terms of the Shapley value contribution of each explanatory variables.

For the implementation of our model, we have used TreeSHAP, a consistent and accurate method, available in open-source packages. TreeSHAP is a fast algorithm that can compute SHapley Additive exPlanation for trees in polynomial time instead of the classical exponential runtime. For the xgboost part of our model we have used NVIDIA GPUs to considerably speed up the computations. In this way, the TreeSHAP method can quickly extract the information from the xgboost model.

Our research has important policy implications for policy makers and regulators who are in their attempt to protect the consumers of artificial intelligence services. While artificial intelligence effectively improve the convenience and accessibility of financial services, they also trigger new risks. Our research suggests that network based explainable AI models can effectively advance the understanding of the determinants of financial risks and, specifically, of credit risks. The same models can be applied to forecast the probability of default, which is critical for risk monitoring and prevention.

Future research should extend the proposed methodology to other datasets and, in particular, to imbalanced ones, for which the occurrence of defaults tends to be rare, even more than what observed for the analysed data. The presence of rare events may inflate the predictive accuracy of such events [as shown in Bracke et al. ( 2019 )]. Indeed, Thomas and Crook ( 1997 ) suggests to deal with this problem via oversampling and it would be interesting to see what this implies in the proposed correlation network Shapley value context.

Bracke, P., Datta, A., Jung, C., & Sen, S. (2019). Machine learning explainability in finance: an application to default risk analysis. Bank of England staff working paper no. 816.

Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 785–794). ACM.

Clauset, A., Newman, M. E., & Moore, C. (2004). Finding community structure in very large networks. Physical review E , 70 (6), 066111.

Article   Google Scholar  

Croxson, K., Bracke, P., & Jung, C. (2019). Explaining why the computer says ‘no’. FCA-Insight.

EU. (2016). Regulation (EU) 2016/679—general data protection regulation (GDPR). Official Journal of the European Union .

Friedman, J., Hastie, T., & Tibshirani, R. (2000). Additive logistic regression: A statistical view of boosting (with discussion and a rejoinder by the authors). Annals of Statistics , 28 (2), 337–407.

FSB. (2017). Artificial intelligence and machine learning in financial services—market developments and financial stability implication . Technical report, Financial Stability Board.

Giudici, P. (2018). Financial data science. Statistics and Probability Letters , 136 , 160–164.

Giudici, P., Hadji-Misheva, B., & Spelta, A. (2019). Network based credit risk models. Quality Engineering , 32 (2), 1–13.

Google Scholar  

Joseph, A. (2019). Shapley regressions: a framework for statistical inference on machine learning models . Research report 784, Bank of England.

Lundberg, S., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, & R. Garnett (Eds.), Advances in neural information processing systems 30 (pp. 4765–4774). Curran Associates, Inc.

Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., & Nair, B., et al. (2020). From local explanations to global understanding with explainable ai for trees. Nature machine intelligence , 2 (1), 2522–5839.

Mantegna, R. N., & Stanley, H. E. (1999). Introduction to econophysics: Correlations and complexity in finance . Cambridge: Cambridge University Press.

Book   Google Scholar  

Molnar, C. (2019). Interpretable machine learning: A guide for making black box models explainable .

Murdoch, W. J., Singh, C., Kumbier, K., Abbasi-Asl, R., & Yu, B. (2019). Definitions, methods, and applications in interpretable machine learning. Proceedings of the National Academy of Sciences , 116 (44), 22071–22080.

Shapley, L. (1953). A value for n-person games. Contributions to the Theory of Games , 28 (2), 307–317.

Thomas, L., & Crook, J. (1997). Credit scoring and its applications. SIAM Monographs .

Download references

Acknowledgements

This research has received funding from the European Union’s Horizon 2020 research and innovation program “FIN-TECH: A Financial supervision and Technology compliance training programme” under the Grant Agreement No 825215 (Topic: ICT-35-2018, Type of action: CSA), and from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No.750961. Firamis acknowledges the NVIDIA Inception DACH program for the computational GPU resources. In addition, the Authors thank ModeFinance, a European ECAI, for the data; the partners of the FIN-TECH European project, for useful comments and discussions. The authors also thank the Guest Editor, and two anonymous referees, for the useful comments and suggestions.

Open access funding provided by Università degli Studi di Pavia within the CRUI-CARE Agreement.

Author information

Authors and affiliations.

University of Pavia, Pavia, Italy

Niklas Bussmann & Paolo Giudici

FinNet-Project, Frankfurt, Germany

Dimitri Marinelli

FIRAMIS, Frankfurt, Germany

Jochen Papenbrock

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Paolo Giudici .

Ethics declarations

Conflicts of interest.

Niklas Bussmann, Dimitri Marinelli and Jochen Papenbrock have been, or are, employed by the company FIRAMIS. The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The paper is the result of a close collaboration between all four authors. However, JP is the main reference for use case identification, method and process ideation and conception as well as fast and controllable implementation, whereas PG is the main reference for statistical modelling, literature benchmarking and paper writing.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Bussmann, N., Giudici, P., Marinelli, D. et al. Explainable Machine Learning in Credit Risk Management. Comput Econ 57 , 203–216 (2021). https://doi.org/10.1007/s10614-020-10042-0

Download citation

Accepted : 17 August 2020

Published : 25 September 2020

Issue Date : January 2021

DOI : https://doi.org/10.1007/s10614-020-10042-0

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Credit risk management
• Explainable AI
• Financial technologies
• Similarity networks
• Find a journal
• Publish with us
• Track your research

Effectiveness of Credit Management System on Micro Credit Performance: Case Study of East Africa Commercial Banks and Mobile Operators

18 Pages Posted: 7 Jun 2021

Eveque Mutabaruka

Atlantic International University

Date Written: May 09, 2021

Credit management is the process of granting credit, terms and conditions definition, compliance with credit policy, and then payment on the due date. The core business for financial institutions is to improve revenues and profit by facilitating sales and reducing loss and financial risks. This research is to assess the effectiveness of credit management principles and their impact on loan performance for a specific type of loan “microcredit”. The case study is microcredit in East Africa especially provided by commercial banks in partnership with mobile network operators. The purpose of the research will be mainly to assess the applicability of credit management principles to achieve better performance in microlending. This research's target sample is East Africa commercial banks in conjunction with mobile operators providing online microcredit to mobile money subscribers and commercial banks ‘customers.

Keywords: credit management, microcredit, loan performance, commercial bank, mobile network operator

Suggested Citation: Suggested Citation

Eveque Mutabaruka (Contact Author)

Atlantic international university ( email ).

Honolulu, HI United States

Do you have a job opening that you would like to promote on SSRN?

Paper statistics, related ejournals, development economics: macroeconomic issues in developing economies ejournal.

Subscribe to this fee journal for more curated articles on this topic

Development Economics: Regional & Country Studies eJournal

Monetary economics: financial system & institutions ejournal.

• Open access
• Published: 05 December 2019

Impact of risk management strategies on the credit risk faced by commercial banks of Balochistan

• Zia Ur Rehman 1 ,
• Noor Muhammad 1 ,
• Bilal Sarwar 1 &
• Muhammad Asif Raz 1  

Financial Innovation volume  5 , Article number:  44 ( 2019 ) Cite this article

63k Accesses

19 Citations

Metrics details

This study aims to identify risk management strategies undertaken by the commercial banks of Balochistan, Pakistan, to mitigate or eliminate credit risk. The findings of the study are significant as commercial banks will understand the effectiveness of various risk management strategies and may apply them for minimizing credit risk. This explanatory study analyses the opinions of the employees of selected commercial banks about which strategies are useful for mitigating credit risk. Quantitative data was collected from 250 employees of commercial banks to perform multiple regression analyses, which were used for the analysis. The results identified four areas of impact on credit risk management (CRM): corporate governance exerts the greatest impact, followed by diversification, which plays a significant role, hedging and, finally, the bank’s Capital Adequacy Ratio. This study highlights these four risk management strategies, which are critical for commercial banks to resolve their credit risk.

Introduction

Credit risk causes economic downturn as banks fail due to default risk from clients, which has had a negative impact on the economic development of many nations around the world (Reinhart & Rogoff, 2008 ). By definition, credit risk describes the risk of default by a borrower who fails to repay the money borrowed. The term hedging signals the protection of a business’s investments by limiting its level of risk, for example, by purchasing an insurance policy. Diversification is the allocation of financial resources in variety of different investments and has also long been understood to minimize such risk. The capital adequacy ratio is a measure of a bank’s capital maintained to absorb its outlying risks. Since there is a lot of competition among banks to attract customers, therefore, it has triggered several innovations in banking services (Aruwa & Musa, 2014 ). Regulators also require banks to improve internal governance practices in order to ensure transparency and ethical standards to keep the customers satisfied with their products and services. Ambiguity in banks’ terms and conditions will make it difficult for customers to select financial products appropriate for their needs, whereas clear terms and conditions allow customers to be more satisfied with the bank’s performance (Ho & Yusoff, 2009 ). Customers expect the financial institutions to have strong policies that can safeguard their interests and protect them. Therefore, poor understanding of effective credit risk and the acceptable risk management strategies by bank managers poses a threat to the commercial banks advancement and customers’ interest.

One critical success factor for financial institutions lies in their realization of the importance of credit risk and devising solid strategies – such as hedging, diversification and managing their capital adequacy ratio – to avoid shortcomings that could lead to operational catastrophe. Credit risks faced by banks have fundamental impact on the performance because, even few large customers default on loans would cause huge problems for it. The objective of the Credit Risk Management (CRM) process is to maximize the cost-adjusted rate of return of a particular bank by maintaining exposure to credit risk acceptable to its shareholders. Banks have to navigate the credit risk associated with the overall portfolio as well as external risks that may be due to macroeconomic factors in the economy. Banks must also compare the credit risk relationships with other risks. Another specific case of credit risk applies to the method of trying to settle banking transactions. Until and unless both parties settle their payments in a timely manner, bank suffers from opportunity loss. Corporate governance may also have large effect on the risk management strategies used by the bank for reducing credit risks. Research suggests that it is imperative that banks engage in prior planning in order to avoid future problems (Andrews, 1980 ).

Majority of commercial banks provide several services that could help them mitigate or manage risk. For example, hedging has been used to reduce the level of risk involved in transactions by keeping specific conditions that would allow different parties to exchange goods or services at a flexible date and time (Harrison & Pliska, 1981 ). The significance of effective risk management strategies have been highlighted by many researchers and practitioners over time to assist banks and other financial institutions. CRM became an obvious necessity for commercial banks, especially after the 2008 global financial crisis, in which it was primarily subprime mortgages that caused a liquidity crisis (Al-Tamimi, 2008 ). According to Al-Tamimi ( 2008 ), ensuring the efficient practice of risk management may not be expensive but the implementation should be done in a timely manner in order to ensure smooth banking operations.

A financial institution, just like a constituent part of any other major economic sector, aims to meet incurred expenses, increase the return on invested capital and maximize the wealth of its shareholders. In their pursuance of these objectives, the financial system has to offer effective risk management strategies to financial institutions like banks against credit risk (Hakim & Neaime, 2005 ).

Problem statement

In 2008, across the world, the credit crisis began as a result of mass issuing of sub-prime mortgages to individuals in the United States leading to defaults, which caused outwardly-rippling problems for financial institutions all across the world. Sub-prime mortgages and other loans with less restrictions can generate remarkable losses including corporate failure and bankruptcy for financial institution (Brown & Moles, 2014 ). These credit decisions have a pivotal role in firms’ profitability. The decision to over-extend credit to high-risk customers may increase short-term profitability for individual banks, though in aggregate, this lending behavior was seen to become a major challenge to the risk management structures of the economy as a whole. Therefore, managing risk is the most important element of a bank’s operations. This phenomenon is equally applicable to banks across the globe, including banks in Pakistan.

Due to unstable and volatile nature of the political and financial environment in Pakistan, banks are affected by many types of risk, including risks to foreign exchange rates, liquidity, operations, credit and interest rates. Pakistan’s financial institutions are generally risk-averse, especially towards car financing and mortgage loans in which chances of huge losses are higher (Shafiq & Nasr, 2010 ). Balochistan is the least developed part with largest geographical area in Pakistan. There are limited opportunities for small businesses and majority of businesses are run in informal form with poor documentation. Majority of commercial banks face problems like loan documents verification and loan processing. Therefore, the adoption of proper risk management strategies can help understand and mitigate the credit risk faced by commercial banks of Balochistan.

Research objective

This study aims to identify the different risk management strategies that can influence the management of credit risk by commercial banks. We expect to determine if these strategies contribute both to the reduction of credit risk as well as the efficient performance in fulfilling customer needs.

Significance of the study

This study aims to provide a basis for guidance for the commercial banks of Balochistan to adopt long-term performance-improving risk management strategies (Campbell, 2007 ). The model for the study shows the impact of risk management strategies, including hedging, diversification, the capital adequacy ratio and corporate governance. The research will also examine the impact of each risk management strategy individually in order to understand the importance of each strategy. To the best of authors’ knowledge, there is no study on credit risk management on Balochistan using the described parameters. The findings of this study are intended to contribute positively to society by demonstrating that the banks of Balochistan can develop effective strategies to improve their CRM. Additionally, policy makers can identify and generate appropriate policies to govern bank behavior in order to minimize risk.

Literature review

Credit risk is considered as the chance of loss that will occur when the loan or any other line of credit by a particular debtor is not repaid (Campbell, 2007 ). Since 2008, financial experts around the world have researched and analyzed the primary factors underpinning the credit crisis to identify problematic behavior and effective solutions that can help financial institutions avoid catastrophe in the future. Long ago, the Basel Committee on Banking Supervision Footnote 1 (1999) has also identified credit risk as potential threat to banking sector and developed certain banking regulations that must be maintained by the banks around the world. Owojori, Akintoye, and Adidu ( 2011 ) stated that there are legislative inadequacies in financial system especially in banking system that are effective as well as lack of uniform credit information sharing amongst banks. Thus, it urges to the fact that banks need to emphasize on better risk management strategies which may protect them in the long run.

Abiola and Olausi ( 2014 ) emphasized on the establishment of a separate credit unit at banks with professional staff for credit/loan officers and field officers. It is important as they perform variety of functions from project appraisals through credit disbursement, loan monitoring to loans collection. Therefore, a comprehensive human resource policy related their selection, training, placement, job evaluation, discipline, and remuneration need to be in placed to avoid any inefficiencies related to loan management and credit defaults.

Ho and Yusoff ( 2009 ) focused on researching Malaysian financial institutions and their management of credit risk. The study involved a sample of 15 foreign and domestic financial institutions from which the data was collected through questionnaires. The findings demonstrated that the diversification of loan services leads to risk improvement, though it requires training employees and the commitment of employees to ensure that the financial institution will meet the requirements for best practice lending.

Brown and Wang ( 2002 ) conducted study about the challenges faced by Australian financial institutions due to credit risk over the period January 1986 to August 1993. The Australian financial institutions were not able to provide a wide variety of alternatives to their clients that led to higher risks as there was a lack of diversification in their services. The research suggested that corporate governance practices allow firms to adopt appropriate rules, policies, and procedures to ensure that the rights of all the stakeholders are fulfilled. Hedging Footnote 2 is used by financial institutions to minimize the risk associated with the transactions conducted with the bank customers as it allows the bank to minimize the risk by offering flexible offers that allows customer to make their decisions effectively (Dupire, 1992 ).

The work of Karoui and Huang ( 1997 ) indicates that the super hedging strategy Footnote 3 could be implemented to achieve a surplus downside market risk as it possesses a duality of both the super hedging and open hedging approaches. The prices of options can increase due to the volatility of the asset prices. If the prices of the financial instrument are fluctuating, then the price of the options contract might also be influenced as the buyers or sellers will be deriving their profit from the price of the financial security (Hobson, 1998 ).

Several factors are associated with the pricing of securities as these factors support the financial decisions that must be made by the investors. The loans that the bank provides to the borrower are highly dependent on the conditions of the market. Decision-making for mitigation and management of credit risk is very important for banks (Li, Kou, & Peng, 2016 ). A highly volatile security market will influence the prices and interest rates of the securities being exchanged in such a market. Financial markets are affected by the macroeconomic variables that influence the prices of the securities being exchanged. Hedging allows firms and their managers to incorporate policies that will maximize the value of the company as clients have a wide array of alternatives that allow them to make their decisions in an effective manner. The derivatives such as options, futures, forwards and swaps that are used by firms increase their financial stability by allowing the customers to have sufficient information that improves their decision making in different circumstances. This enables managers to adopt practices that will benefit their organizations. Hedging allows businesses to support a higher debt load due to its flexible nature and ability to minimize risk, which increases the value of the company as it can actually meet the needs of more customers with a comparatively lower level of risk (Graham & Rogers, 2002 ). Similarly, Levitt ( 2004 ) explained that hedging enables firms to extend its activities because the risk inherent to providing funds is reduced in such transactions, allowing more flexibility to all involved parties.

Banks are able to maintain a particular level of reserved cash for the sake of managing the day to day operations that is decided based on the allocated capital adequacy ratio. This enables the bank to maintain a balance of cash that is sufficient to meet the needs of the customers. Managers can use the bank’s available cash flow to meet short-term cash requirement needs, which are based on the concept of capital adequacy ratio. This gives certainty to some funds that banks must maintain in order to address unforeseen circumstances. The selective hedging concept has been used by firms for the sake of making investments that are based on a certain part of their portfolio that pose the most threat and not the entire portfolio of the financial instruments (Stulz, 1996 ). The emphasis is on utilizing hedging at the right time for the specific customer that a company believes should be entering into a contract with flexible terms and conditions. It is a viable option for banks to use hedging to avoid customers’ dissatisfaction for those who do not meet the firm’s loan eligibility criteria. Zhang, Kou & Peng, ( 2019 ) proposed a consensus model that considers the cost and degree of consensus in the group decision making process. With a certain degree of consensus the generalized soft cost consensus model was developed by defining the generalized aggregation operator and consensus level function. The cost is properly reviewed from the perspective of the individual experts and the moderator. Economic significance of the two soft consensus cost models is also assessed. The usability of the model for the real-world context is checked by applying it to a loan consensus scenario that is based on online data from a lending platform. Group decision making is critical for changing the opinions of everyone to arrive at a synchronized strategy for minimizing the risks of the bank with the help of hedging (Zhang, Kou, & Peng, 2019 ).

Kou, Chao, Peng, Alsaadi & Herrera-Viedma, ( 2019 ) identified that financial systemic risk is a major issue in financial systems and economics. Machine learning methods are employed by researchers that are trying to respond to systemic risks with the help of financial market data. Machine learning methods are used for understanding the outbreak and contagion of the systemic risk for improving the current regulations of the financial market and industry. The paper studies the research and methodologies on measurement of financial systemic risk with the help of big data analysis, sentiment analysis and network analysis. Machine learning methods are used along with systematic financial risk management for controlling the overall risks faced by the banks that are related to hedging of the financial instruments of the bank (Kou, Chao, Peng, Alsaadi, & Herrera-Viedma, 2019 ).

Provision of financial assistance to customers that require the funds for business activity can prove profitable for the bank (Datta, Rajagopalan, & Rasheed, 1991 ). If the principle and interest of the loan is repaid in a timely manner that would help the banks ensure smooth flow of their operations, and the economic activities in the society are improved as the standard of living of people also improves with such financial assistance that is provided by commercial banks (Keats, 1990 ). As banks enter into such contracts with several customers, the level of the its incurred risk increases; management likewise becomes more complex with a more diverse group of customers (Kargi, 2011 ). Non-Performing Loans (NPL) represent the credit that a bank believes is causing a loss, and includes loan defaults, which are typically categorized by their expectation of recovery as “standard,” “doubtful” or “lost” (Kolapo, Ayeni, & Oke, 2012 ). The lost category focusing on the inability of the bank to recover particular products restricts a bank from reaching the set targets thus causing a bank to fail in attaining the objectives of profitability that have been set. The incurrence of a large amount of high-risk debt is often difficult for banks to manage unless the managers have undertaken appropriate strategies for mitigating the risk in addition to enhancing their financial performance. The existence of NPLs prompted central global banks to enter into the 1988 Basel Accord, also known as Basel I (later superseded in 2004 by Basel II), which maintained that banks must maintain a particular amount of capital in order to meet their operational needs (Van Greuning & Brajovic Bratanovic, 2009 ). This on-hand capital requirement, also called the capital adequacy ratio, is beneficial as it allows banks to more easily manage potential, sudden financial losses (Keats, 1990 ).

Kithinji ( 2010 ) provides specific evidence that the management of credit risk does not influence the profitability of banks in Kenya. In fact, the Kargi ( 2011 ) study on Nigerian banks from 2004 to 2008 revealed a healthy relationship between appropriate CRM (Credit Risk Management) and bank performance. Poudel ( 2012 ) emphasized the significant role played by CRM in the improvement of financial performance of banks in Nepal between 2001 and 2011. Strict requirements of maintaining higher capital that is around 14.3% of the cash balance as reserve in the banks of Nepal was found to have resulted in better bank performance by producing more profit.

Heffernan ( 1996 ) stated that CRM is crucial for predicting proper bank financial performance. A bank’s inability to recoup its outstanding loans reduces its ability to engage in other profitable transactions A loss both of principle as well as interest (including time value) means also a loss in opportunities to expand and pursue other profitable operations (Berríos, 2013 ).

Banks that avoid risk management face several challenges, including their own survival in the current highly competitive financial environment. To compete successfully with other commercial financial institutions, banks rely on a diversification of products and financial services to improve portfolio performance, including attracting more customers. Diversified services allow customers to select the most appropriate financial assistance in light of their individual needs. Along with diversification of the financial services, banks need to manage the credit risk involved where funds are given as loans for various needs of the customers such as car loans, house loans, starting a new business or expanding ongoing business (Kou, Ergu, Lin, & Chen, 2016). It is also important to have effective behavior monitoring models to ensure that bank employees are careful in minimizing the operational risks by providing maximum information to the customers about the financial instruments and the restrictions imposed by the bank for the sake of protecting the interests of the financial institution. Chao, Kou, Peng & Alsaadi, ( 2019 ) conducted a study to understand a new form of money laundering that is trade based which is using the signboard of international trade. It appears along with the capital movement that is mostly concerned with the rise in the collapse of the overall financial market. It is difficult to prevent money laundering since it has a plausible sort of trade characterization. The aim of the paper is to develop monitoring methods that have accurate recognition along with classified form of supervision of the trade based money laundering with the help of multi class knowledge driven classification algorithms that are linked with the micro and macro prudential regulations. Based on an empirical study from China the application is reviewed and the effectiveness is assessed in order to improve the efficiency of the management in the financial markets (Chao, Kou, Peng, & Alsaadi, 2019).

Selecting the most eligible customers for a loan is also essential to managing credit risk: a bank can screen through a list of customers to identify the ones who have a higher probability of repayment within the specified time duration, according to the terms and conditions of the contract. Hentschel and Kothari ( 1995 ) emphasized that using different derivatives is significant for the leverage of the financial institution. A vast majority of companies surveyed were using derivatives to reduce their risk (Kou, Peng, & Wang, 2014 ). Dolde, ( 1993 ) highlighted that several banks are vulnerable to various risks, therefore, banks have undertaken specific precautionary measures like training their employees, developing better credit policies and reviewing the credit rating of the customers applying for the loans (Dolde, 1993 ).

Diversification is adopted by corporations for increasing the returns of the shareholders and minimizing risk. Decision-making criteria is improved by using classifiers that have some algorithms for resolving problems (Kou, Lu, Peng, & Shi, 2012 ). Rumelt ( 1974 ) revealed that only around 14% of firms on the Fortune 500 list were working as single business organizations in 1974, whereas 86% of the businesses operated in diversified product markets. This shows a considerable inclination of the business sector to emphasize diversification instead of single trade. Much research has been conducted focusing on the activities of companies during recent times; most have found a rise in the prevalence of diversified firms (Datta et al., 1991 ).

Research hypotheses

The first hypothesis considers assessing the role of hedging in reducing a bank’s credit. Based on a model presented by Felix ( 2008 ), which showed risk management strategies of hedging, capital adequacy ratio and diversification may be used to explain credit risk that a bank faces. Thus our first hypothesis is as follows:

H 1 : hedging will minimize credit risk faced by the commercial banks of Balochistan

The second risk management strategy is diversification, which requires banks to provide a wide range of financial services with flexible terms to customers and to provide credit to a wide range of customers instead of few in order to reduce risk (Fredrick, 2013 ). The concept of diversification can be used by banks as they create a wide customer pool for providing loans, instead of providing large amount of loans to few customers, which inherently increases risk (Hobson,  1998 ). Therefore,

H 2: diversification will minimize credit risk of the commercial banks of Balochistan

The third hypothesis considers management strategy that requires banks to maintain a particular amount of the capital (Ho & Yusoff, 2009 ). The capital adequacy ratio is critical for banks to be in a better position to manage unexpected risks and thus capital maintained in a bank has a consequence at overall credit risk therefore the it may be hypothesized as following:

H 3 : capital adequacy ratio will minimize credit risk of commercial banks of Balochistan

The fourth hypothesis considers the role played by corporate governance in minimizing credit risk. Corporate governance assumes that the organization or corporation should adopt all practices that ensure accountability to the stakeholders (Shafiq & Nasr, 2010 ). Therefore,

H 4 : corporate governance will minimize credit risk of the commercial banks of Balochistan

Methodology.

This study adopts an explanatory research design, which was aimed to collect authentic, credible and unbiased data. The data were collected from the employees of commercial banks located in the province of Balochistan, Pakistan. All ethical considerations were made during the research process. The questionnaire developed for the collection of information was prepared to effectively incorporate all potential factors that include, diversification, hedging, capital adequacy ratio, corporate governance and credit risk. The purpose of this research was clearly explained in the questionnaire as it was being shared with the respondents.

The participants were informed about the research objective and ensured that the information provided would be kept confidential. This step was designed to remove bias and ensure that the participants were able to share their views without having any reservations. This process is important for authentic results and reliable information (Levitt, 2004 ).

The sample size for this study comprised of 250 employees from commercial banks in Balochistan. There are large scale commercial banks that operate in Pakistan with several branches of these banks working in the entire country. Commercial banks approached for this study included Habib Bank Limited, Standard Chartered Bank, United Bank Limited, Summit Bank, Faisal Bank, Askari Bank and Bank Al-Habib.

The questionnaire was adopted from a global survey previously conducted by the World Bank. This study analyzed the work that has been done on managing credit risk in several countries in different parts of the world. Our questionnaire used the framework of this valuable research tool, adopting changes specific to address the localized context of Balochistan.

The information collected from the participants was analyzed to identify trends and practices in the banks operating in Balochistan to understand the practices of these commercial banks for managing credit risk. Following is the theoretical framework of the study.

The relationships between risk management strategies such as diversification, hedging, the capital adequacy ratio and corporate governance with credit risk itself were determined in the paper.

Results & findings

The questionnaire was tested to check the reliability through Cronbach’s alpha (Table 1 ), which shows internal consistency of the instrument; the information revealed that the data are 80% reliable, considering the total of 31 questions asked. The information is essential as this shows that the results and findings of the study are reliable and they can be generalized to the population (Hungerford, 2005 ).

The correlation table shows the relationship between the different variables in the research study. The dependent variable, credit risk, was reviewed against the independent variables: corporate governance, hedging, diversification and capital adequacy ratio. The correlation is essential for further analysis as there should be some relation between the different variables. Each variable is used for the correlation analysis so it highlights the correlation among all the variables with each other. This is useful for assessing the correlation among the independent variables and to ensure that it is not too high leading to a problem of multicollinearity.

Table  2 shows the results of the correlation test between the independent variables and the dependent variable. Before running regression analysis, basic assumptions were also checked. Data normality was checked through skewness and kurtosis and for all variables; these values were in range ± 2. Linearity was checked through correlation analysis and all variables were shown to have a significant relationship with each other. Homogeneity was checked through scatter plot, showing that the variance across all variables was the same. No autocorrelation was found as the value for the Durbin Watson test was 2, showing no correlation among residuals (Antonakis, Bendahan, Jacquart, & Lalive, 2014 ). The value for the variance inflation factor (VIF) was VIF < 5, which shows no relationship among the four independent variables. The regression test was used to determine the influence of each of the variable on credit risk. The results can be seen in Table 3 .

Credit risk can be influenced by different factors but, there is around 36% influence of the four variables that are independent. The variation of 36% can be explained by the independent variables that are hedging, diversification, capital adequacy ratio and corporate governance on credit risk. These factors account for this much change that can be observed in the credit risk faced by the commercial banks. The adjusted r 2 was further analyzed because it is a better measure for a focused analysis on a bank’s performance.

Table  4 shows the results of the assessment of the data for the overall model goodness of fit; the overall model is highly significant at p  < 0.05. The analysis of the variance across the small samples of the data reveals that the overall information is consistent.

The standardized coefficients in Table 5  show the rate of change that is caused by each of the variables in the credit risk of the commercial banks. This is critical information as the variable that is having a higher coefficient value will be having more influence on the level of credit risk so it should be emphasized more by the commercial banks for the sake of achieving better performance. The regression analysis highlights that the four independent variables have an impact on credit risk.

The results reveal that corporate governance had the most impact on credit risk (with a 0.288 standardized beta value). In other words, this CRM strategy appears to be the most beneficial for commercial banks to undertake. Next is diversification (0.263 beta), followed by hedging (0.250 beta) and, finally, the capital adequacy ratio (0.040 beta). The results are significant in is showing that these variables have an impact on credit risk. The constant value was calculated at 1.765 and the error term in the equation is 0.237.

Recommendations

The banks in Balochistan would benefit from adopting sound strategies to improve control over credit risk. CRM strategies such as diversification, hedging, corporate governance and the capital adequacy ratio have all been cited in extant research as being crucial for the success in this regard; in fact, many problems arising from credit risk can be resolved by implementing some combination of these strategies. The research findings can likewise help the government of Balochistan to ensure that commercial banks take appropriate risk management measures to help keep them from failures, such as falling into bankruptcy (Greuning & Bratanovic, 2009 ). Society depends on the smooth operation of the banking sector, so individual (and aggregate) bank performance can help contribute to the development and improved welfare of the economy. Therefore, effective inspection should be employed by the banks to check and safeguard bank resources. Effective trainings and refresher courses should be giving to bank employees in the areas of risk asset management, risk control and credit utilization in order to ensure proper usage and performance.

Several banks have failed in the past as they were not able to control their credit risk. Recommendations for banks stemming from this study include the diversification of their products and services, which is critical as it allows the bank to provide customers with many products and services. After diversification, an emphasis on employing corporate governance policies is most important, according to the findings. Hedging and the capital adequacy ratio are also important strategies that can be examined and optimized by banks. Hedging is useful because entering into flexible contracts helps reduce risk. The banks in Balochistan will be able to realize the importance of the capital adequacy ratio as that will allow them to achieve a proper balance between the amounts of capital that should be maintained to manage the needs of the investors. It is recommended that further research on the topic should be conducted so that effective strategies for management of other risks can be identified for banks. The success and further progress of these banks depend on the smooth implementation of risk management strategies and activities, which have been shown to have a very significant positive impact on the ability of the banks of Balochistan to control credit risk.

Availability of data and materials

The data of the research paper will be available upon request.

This is a place in Switzerland where the Basel Committee on Banking Supervision (BCBS) comprising of 45 members from 28 Jurisdictions, consisting of Central Banks and authorities have the responsibility of banking regulation.

Hedging are flexible contracts that allow customers to agree to buy a particular product in future date using spot rates. It allows customers and banks to manage the transaction by locking contracts at desired price.

Super hedging strategy allows the users to hedge their positions with a trading plan based on self-financing. A low price is paid for the portfolio that would ensure that it’s worth to be equal or higher at a future date.

Acknowledgements

We are grateful to all the reviewers who have shared their valuable comments and suggestions for the research paper. The Editorial Board of Financial Innovation has been extremely kind in their editorial efforts.

There was no funding required for the completion of the research paper.

Author information

Authors and affiliations.

Balochistan University of Information Technology Engineering & Management Sciences, Quetta, Pakistan

Zia Ur Rehman, Noor Muhammad, Bilal Sarwar & Muhammad Asif Raz

You can also search for this author in PubMed   Google Scholar

Contributions

NM is the corresponding author and he has also given the idea for the paper. NM has reviewed the theoretical framework and empirical analysis of the research paper. ZR has written the manuscript and collected the data for the paper. BS has reviewed the methodology of the paper and reviewed literature. MAR has given conception advice and edited the paper. All authors have read the paper and approved the final manuscript.

Corresponding author

Correspondence to Noor Muhammad .

Ethics declarations

Competing interests.

The authors declare that they have no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Reprints and permissions

About this article

Cite this article.

Rehman, Z.U., Muhammad, N., Sarwar, B. et al. Impact of risk management strategies on the credit risk faced by commercial banks of Balochistan. Financ Innov 5 , 44 (2019). https://doi.org/10.1186/s40854-019-0159-8

Download citation

Received : 06 February 2019

Accepted : 08 November 2019

Published : 05 December 2019

DOI : https://doi.org/10.1186/s40854-019-0159-8

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Credit risk
• Risk management strategies
• Financial risk

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

• We're Hiring!
• Help Center

An assessment of credit management and loan performance in Bank of Abyssinia

Related Papers

Mujammel Shipon

Tekeste Gebrekidan

Endale Kiflu

JURNAL ILMIAH AKUNTANSI UNIVERSITAS PAMULANG

Nopri Ismanda

The purpose of this research is to know the procedure of giving workingcapital loan at Bank Sumsel Babel, to know whether the procedure of Working Capital Credit at Bank Sumsel Babel has been run in accordance with prevailing regulation in Bank Sumsel Babel, and also effort to prevent problem loans through operational audit. The research method used in this paper is descriptive analysis with data collection techniques in the form of observation and interview with Credit Division Bank Sumsel Babel. The results of this study indicate that from the five stages in the provision of working capital loans, there are several findings that exist at the stage of credit analysis and in the credit supervision stage. In the credit analysis phase of credit application instrument documents (PAK) in the form of MPK and FIP is not prepared in accordance with the guidance book of credit (BPP) of Bank Sumsel Babel. In the credit supervision stage, credit analyst of Bank Sumsel Babel does not compose F...

jean claude karekezi

International Journal of Finance and Accounting

michael njeru

Purpose: the purpose of this study was to analyze effectiveness of credit management system on loan performance of commercial banks in Kenya.Methodology:Descriptive research design was used. The population comprised of 86 respondents. That is, one credit manager and one credit officer from one branch of each of the 43 commercial banks registered with central bank of Kenya as at this year. A census study was conducted since the target population was small. Data was collected using a self-administered questionnaire through drop and pick later method. The questionnaire was both open and closed ended. Test retest method was used to ensure reliability while piloting was used to check the validly of the research instrument .data was analyzed using to frequencies, percentages and means. Correlation was used to compute the degree of association between variable. The hypothesis was tested using chi square. Data was thereafter presented using table and pie charts. Results:The study establishe...

Sebastian Gasana

Dennis Appiah-Sasraku

ABSTRACT The purpose of this study was to determine the impact of credit assessment process on loan repayment. When proper evaluation of the customer is performed it measures the financial condition and the ability of the customer to repay the loan in future. Credit Assessment is a process to ascertain the risks associated with the extension of the credit facility. It is generally carried by the financial institutions which are involved in providing financial funding to its customers. The parameters considered under the credit appraisal processes were; the 5C’s Process, 5P’s Process, CAMPARI Process, LAPP Process and PACT Process. The factors in the credit assessment process that were analyzed include; policy, screening, appraisal and review. A sample size of 22 was selected for this research with 20 answering the questionnaire and 2 interviewed. The study concluded that no one credit appraisal technique was used in isolation, with the bank opting to use more than one of each of the credit appraisal techniques. The study also concluded that the most commonly used process however was the 5C’s followed by 5P’s and CAMPARI. In this paper, we study the Credit Assessment Model of Fidelity Bank, Ghana and also, seek to observe the effort of the bank’s credit department to reduce the various inherent risk parameters. The study revealed that there were weaknesses in credit appraisal policies, which allowed rogue bank staff to award loans to non-qualifying applicants. The respondents however agreed the drafting of appraisal policies involved the input of bank staff. The study also revealed that the bank is yet to fully integrate computerized its credit appraisal techniques. The scope of the study is restricted to the Head Office of Fidelity Bank, Ghana.

Willy Muturi

Selam Abebe

Loading Preview

Sorry, preview is currently unavailable. You can download the paper by clicking the button above.

RELATED PAPERS

International Journal of Advanced Research (IJAR)

IJAR Indexing

Unpublished

Emmanuel D A R E Otitolaiye

Marta Girma

Noble International Journal of Business and Management Research

Ethiopian Civil Service University

Mesfin Sahle

ABC Research Alert

Ndayishimiye Eugene

Olubukola Uwuigbe

International journal of business and social research

Simon Waithaka

IFTAKHARUL ISLAM

Uttam Golder

International Journal of Current Aspects

Evans Brako Ntiamoah

Fabrice Tchakounte Kegninkeu

International Journal of Trend in Scientific Research and Development

FREDRICK NDEDE

helen legesse

Mohiuddin Ahmed

International journal of scientific and research publications

Hanna Bogale

RELATED TOPICS

•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024