International Journal of Nonlinear Analysis and Applications

Machine learning support to provide an intelligent credit risk model for banks' real customers

Document Type : Research Paper

Authors

1 Department of Accounting, Qeshm Branch, Islamic Azad University, Qeshm, Iran

2 Department of Accounting, Science and Research Branch, Islamic Azad University, Tehran, Iran

3 Department of Accounting and Finance, Qeshm Branch, Islamic Azad University, Qeshm, Iran

Abstract

Artificial intelligence plays an important role in the field of personal computers. Right now personal computers are part of our lives, so AI should be used in all everyday tasks. Humans are great thinkers, but machines can be more effective at counting than humans. The machine cannot fully explain different conditions, but it can create a different type of connection between different salient points and features. Either way, there can be many benefits to establishing machine learning computing in our daily lives. Machine learning or machine learning is one of the subsets of artificial intelligence that enables systems to learn and improve automatically without explicit programming, and controlling the credit risk of real bank customers is one of these benefits that can help the monetary and banking system to improve conditions and reduce risk. Therefore, the use of machine learning to create an algorithm to manage credit risks is a topic addressed in this research.

Keywords

[1] P.M. Addo, D. Guegan and B. Hassani, Credit risk analysis using machine and deep learning models, Risks 6 (2018), no. 2, 38.
[2] L. Allen and A. Saunders, Incorporating systemic influences into risk measurements: a survey of the literature, J. Financ. Serv. Res. 26 (2004), 161–191.
[3] A. Alonso and J.M. Carbo, Machine learning in credit risk: Measuring the dilemma between prediction and supervisory cost, Banco de Espana Working Paper No. 2032, https://ssrn.com/abstract=3724374, (2020).
[4] O. Aydemir and E. Demirhan, The relationship between stock prices and exchange rates: Evidence from Turkey, Int. Res. J. Finance Econ. 23 (2009), no. 2, 207–215.
[5] N. Bussmann, P. Giudici, D. Marinelli and J. Papenbrock, Explainable machine learning in credit risk management, Comput. Econ. 57 (2021), 203–216.
[6] S. Chamberlain, J.S. Howe and H. Popper, The exchange rate exposure of US and Japanese banking institutions, J. Bank. Finance 21 (1997), no. 6, 871–892.
[7] J. Chen, S. Chokshi, R. Hegde, J. Gonzalez, E. Iturrate, Y. Aphinyanaphongs and D. Mann, Development, implementation, and evaluation of a personalized machine learning algorithm for clinical decision support: Case study with shingles vaccination, J. Med. Internet Res. 22 (2020), no. 4, e16848.
[8] V. Flamini, C. Mcdonald and L. Schumacher, The determinants of commercial bank profitability in sub-Saharan Africa, Inte. Monetary Fund Work. Paper No. 09/15, (2009).
[9] R. Habibi, M.A. Rostgar and N. Taghizadeh, Time-varying behavior of optimal credit risk, financial studies and Islamic banking, Quart. Stud. Bank. Manag. Islamic Bank. 6 (2019), no. 14, 63–80.
[10] J. Hall, Fundamentals of financial engineering and risk management, Translated by S. Sayah and A. Salehabadi, Tehran: Tadbir Pardaz Computer Group, 2004.
[11] H. Helbekkmo, A. Kshirsagar, A. Schlosser, F. Selandari, U. Stegemann and J. Vorholt, Enterprise risk management—shaping the risk revolution, New York: McKinsey & Co., 2013.
[12] P.S. Hoffmann, Determinants of the profitability of the us banking industry, Int. J. Bus. Soc. Sci. 2 (2011), no. 22.
[13] R.V. Krejcie and D.W. Morgan, Determining sample size for research activities, Educ. Psychol. Measur. 30 (1970), no. 3.
[14] C.C. Lee and C.C. Lee, Oil price shocks and Chinese banking performance: do country risks matter?, Energy Econ. 77 (2019), 46–53.
[15] X. Ma and S. Lv, Financial credit risk prediction in internet finance driven by machine learning, Neural Comput. Appl. 31 (2019), no. 12, 8359–8367.
[16] G. Mesbahi Moghaddam, G. Asadi and S.A. Sajadi, Conceptual model of Islamic commercial paper for banks and credit institutions on the basis of Murabaha Sukuk, Money Econ. 10 (2015), no. 3, 71.
[17] MetricStream, The chief risk officer’s role in 2018 and beyond managing the challenges and opportunities of a digital era new roles of the CRO, https://www.metricstream.com/insights/chief-risk-officer-role-2018.html, 2018.
[18] A. Mohammad Rahimi and M. Mohammadi, Examining the position of risk management in the financial institution and providing an optimal model for it, First Int. Cong. Risk Manag., Tehran, 2016.
[19] V. Moscato, A. Picariello and G. Sperlı, A benchmark of machine learning approaches for credit score prediction, Expert Syst. Appl. 165 (2021), 113986.
[20] C. Qin, Y. Zhang, F. Bao, C. Zhang, P. Liu and P. Liu, XGBoost optimized by adaptive particle swarm optimization for credit scoring, Math. Prob. Engin. 2021 (2021), 1–18.
[21] C.A. Rajan and R. Baral, Adoption of ERP system: An empirical study of factors influencing the usage of ERP and its impact on end user, IIMB Manag. Rev. 27 (2015), no. 2, 105–117.
[22] S.M. Ryan and A.C. Worthington, Time-varying market, interest rate and exchange rate risk in Australian bank portfolio stock returns: a garch-m approach: Discussion paper no 112, Queensland University of Technology, 2002. [23] A. Saunders and L. Allen, Credit Risk Measurement: New Approaches to Value at Risk and Other Paradigms, John Wiley & Sons, 2002.
[24] S. Shi, R. Tse, W. Luo, S. D’Addona and G. Pau, Machine learning-driven credit risk: a systemic review, Neural Comput. Appl. 34 (2022), no. 17, 14327–14339.
[25] R. Shiva and H. Mikaelpour, Banking risk management, lecture and paper collection, Fourth Islamic Banking Conference, Tehran: Iranian Institute of Banking, First Edition, 2004.
[26] D. Tripathi, D.R. Edla, V. Kuppili and A. Bablani, Evolutionary extreme learning machine with novel activation function for credit scoring, Eng. Appl. Artif. Intell. 96 (2020), 103980.
[27] B. Van Liebergen, Machine learning: A revolution in risk management and compliance?, J.Financ. Transform. Machine Learn. Complian. 45 (2017), 60–67.
[28] O. Wyman, Next generation risk management, //www.oliverwyman.com/content/dam/oliverwyman/v2/publications/2017/aug/Next Generation Risk Management Targeting A-Technology Dividend.pdf.
[29] L. Yu, X. Zhang and H. Yin, An extreme learning machine-based virtual sample generation method with feature engineering for credit risk assessment with data scarcity, Expert Syst. Appl. 202 (2022), 117363.
[30] Y. Zhu, L. Zhou, C. Xie, G.J. Wang and T.V. Nguyen, Forecasting SMEs’ credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach, Int. J. Product. Econ. 211 (2019), 22–33.
International Journal of Nonlinear Analysis and Applications
Volume 15, Issue 4
April 2024
Pages 23-42
  • Receive Date: 18 June 2022
  • Revise Date: 02 September 2022
  • Accept Date: 11 September 2022