Volume 12, Issue 45 (11-2021)                   jemr 2021, 12(45): 199-230 | Back to browse issues page


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Motedayen N, Nazarian R, Damankeshideh M, Seifi pour R. Designing a Comparative Model of Bank Credit Risk Using Neural Network Models, Survival Probability Function and Support Vector Machine. jemr 2021; 12 (45) : 6
URL: http://jemr.khu.ac.ir/article-1-2256-en.html
1- Islamic Azad University
2- Islamic Azad University , r-nazarian@yahoo.com
Abstract:   (1507 Views)
Credit risk is the probability of default of the borrower or the counterparty of the bank in fulfilling its obligations, according to the agreed terms. In other words, uncertainty about receiving future investment income is called risk, which is of great importance in banks. The purpose of this article was to estimate the credit risk of Mellat Bank's legal customers. In this study, the statistical information of 7330 real customers was used. In this regard, the results of neural network model and support vector machine model have been compared. The obtained results have shown that the components considered in this study based on personality, financial and economic characteristics had significant effects on the probability of customer default and credit risk calculation. Also, the results of this study showed that the application of control policies at the beginning of the repayment period suggests facilities that have the highest probability of default with long life and high repayment. Comparing the results obtained from the prediction accuracy of different models, it was observed that the explanatory power of the support vector machine model and the use of the survival probability function was higher than that of the simple neural network model for the studied groups of real customers.
Article number: 6
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Type of Study: Applicable | Subject: رشد و توسعه و سیاست های کلان
Received: 2022/06/1 | Accepted: 2022/07/24 | Published: 2022/11/6

References
1. Abdoli, Ghahraman and Fardhariri, Alireza (2014), Modeling credit risk assessment of legal clients of Refah Bank, Quarterly of Applied Economic Theory, 1, 1-24. (In Persian)
2. Altman, E. I. (1968). Financial Rations, Discriminant Analysis and the Prediction of Corporate Bankruptcy. Journal of Finance, 23, 589-609. [DOI:10.1111/j.1540-6261.1968.tb00843.x]
3. Arab Mazar, Abbas and Ruin Tan, Pune (2004), factors affecting the credit risk of bank customers; A case study of Keshavarzi Bank, Economic Research Quarterly, 6, 45-80. (In Persian)
4. Basel Committee on Banking Supervision, Best Practices for Credit Risk Disclosure, September 2000.
5. Beaver, W. (1967). Financial Ratio as Predictors of Failure, Empirical Research in Accounting: Selected Studies 1966. Journal of Accounting Research, 4, 71-111. [DOI:10.2307/2490171]
6. Bolton, P., Chen, H. and Wang, N. (2009), "A unified theory of Tobin's q, corporate investment, financing, and risk management", The Journal of Finance, Vol. 66 No. 5, pp. 1545-1578. [DOI:10.1111/j.1540-6261.2011.01681.x]
7. Chen, W, Xiang, G. Liu, Y. Wang, K. (2016). Credit risk Evaluation by hybrid data mining technique. Systems Engineering Procedia, 3(0), 1, 20-94.
8. Dargahi Hassan, Ghasemi Mojtabi, Fethullahi Sajjad (2019), the effect of bounced check financial crime on banks' credit risk and economic growth with an emphasis on the law enforcement index: a provincial panel approach, Economic Modeling Research, 11 (40), 7-32 . (In Persian) [DOI:10.29252/jemr.10.40.7]
9. Elmer, P. J. and Borowski, D. M. (1988). "An Expert System and Neural Networks Approach to Financial Analysis". Financial Management, 12, 66-76. [DOI:10.2307/3666073]
10. Emel, Ahmet Burak. Oral, Muhittin. Reisman, Arnold. Yolalan, Reha. (2003). A credit scoring approach for the commercial banking sector. Socio-Economic Planning Sciences, 37, 103-123. [DOI:10.1016/S0038-0121(02)00044-7]
11. Eskandari, Maitham Jafari and Rouhi, Milad (2015), credit risk management of bank customers using decision vector machine method improved by genetic algorithm with data mining approach, Asset Management and Financing Quarterly, 1, 12-38. (In Persian)
12. Feng, Z. (2016). China Microfinance Industry Assessment Report. China Association of Microfinance.
13. Hitchins J Hogg M and Mallett D (2001) Banking: A Regulatory Accounting and Auditing Guide (The Institute of Chartered Accountants).
14. Isazadeh Saeed, Ariani Bahare (2009), ranking of legal clients of banks according to credit risk by data coverage analysis method: a case study of branches of Agricultural Bank, Economic Research and Policy Quarterly, 18 (55), 59-86. (In Persian)
15. Kumar, M., Kumar, P., Kumar, A. Anil Kumar, Ahmed Elbeltagi & Alban Kuriqi (2022), Modeling stage-discharge-sediment using support vector machine and artificial neural network coupled with wavelet transform, Applied Water Science, 12, 87. [DOI:10.1007/s13201-022-01621-7]
16. Liao, A. B. (2015). A Credit Rating Approach for the Commercial Banking Sector. Journal of Socio-Economic Planning Sciences, 37, 45-58.
17. Mirghfouri Seyedhabib Alah and Ashuri Zohra (2014), credit risk assessment of bank customers, Business Management Research Quarterly, 7, 13, 147-166. (In Persian)
18. Mirzaei, Hossein, Nazarian, Rafik and Bagheri, Rana, (2018), Investigating factors affecting the credit risk of legal entities of banks (a case study of branches of the National Bank of Iran, Tehran), Economic Research Trends Quarterly, 19th year , 58, 67-98. (In Persian)
19. Mousavi, Seyedreza and Qolipour, Elnaz (2018), Rating of validation criteria of bank customers with Delphi approach, the first international conference on marketing of banking services. (In Persian)
20. Naji Esfahani, Seyed Ali and Rastgar, Mohammad Ali (2017), Estimating customers' credit risk using multidimensional analysis of preferences (case study: a commercial bank in Iran), Economic Modeling Quarterly, 12(44), 143-161. (In Persian)
21. Paula Matias Gama, Ana & Susana Amaral Geraldes, Helena (2014), Credit Risk Assessment and the Impact of the New Basel Capital Accord on Small and Medium-sized Enterprises: An Empirical Analysis, Management Research Review.
22. Rostamzadeh Parviz, Shahnazi Rohollah, Nissani Mohammad Sadegh (2017), Identification of factors affecting credit risk in Iran's banking industry using stress test, Economic Modeling Research, 9 (32), 91-128. (In Persian) [DOI:10.29252/jemr.8.32.91]
23. Salahi, Mohammad (2017), review and prioritization of effective factors for credit evaluation of bank customers using AHP method (Case: Sina Bank), School of Management, Department of Financial Affairs, University of Tehran. (In Persian)
24. Sanders, A. & Allen, L. (2002). Credit Risk Measurement. Second Edition, NewYork: John Wiley & Sons.
25. Shi-chen, Sh.; Yousefi, N. & Qorbannezhad, J. (2011). "The Study of Effective Factors of Default Bank Credit Facilities (the case study of Legal Customers of Export Development Bank of Iran)". Journal of Financial Knowledge of security analysis, 2: pp. 111-137.
26. Suryanto H, Mahidadia A, Bain M, Guan C and Guan A (2022), Credit Risk Modeling Using Transfer Learning and Domain Adaptation. Front. Artif. Intell. 5:868232. doi: 10.3389/frai.2022.868232. [DOI:10.3389/frai.2022.868232]
27. Tehrani, Reza and Fallah Shams, Mirfaiz (2014), Designing and explaining the credit risk model in the country's banking system, Journal of Social and Human Sciences of Shiraz University, 43, 45-60. (In Persian)
28. Treacy, William F, Carey Mark s (1998), credit risk rating at large U.S banks, Journal of Banking and Finance. [DOI:10.17016/bulletin.1998.84-11]
29. West, S, A. (2014). "Credit Risk Model and ranking Legal Clients of the Agriculture Bank". Economic Journal, 4: 99-128.
30. Ying Zhou, Mohammad Shamsu Uddin, Tabassum Habib, Guotai Chi & Kunpeng Yuan (2021), Feature selection in credit risk modeling: an international evidence, Economic Research-Ekonomska Istraživanja, 34:1, 3064-3091, DOI: 10.1080/1331677X.2020.1867213. [DOI:10.1080/1331677X.2020.1867213]
31. Zhou, Ying, Mohammad Shamsu Uddin, Tabassum Habib, Guotai Chi & Kunpeng Yuan (2021), Feature selection in credit risk modeling: an international evidence, Economic Research-Ekonomska Istraživanja, 34:1, 3064-3091. [DOI:10.1080/1331677X.2020.1867213]

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