Analyzing Customers Credit Scoring Criteria in Banking Industry Using Fuzzy Cognitive Mapping Approach
الموضوعات :
Samaneh ShariatMadari
1
,
Mahdi Homayounfar
2
,
Keyhan Azadi
3
,
Amir Daneshvar
4
1 - Phd Student, Department of Industrial Managemnet, Rasht Branch, Islamic Azad University, Rasht, Iran
2 - Assistant Professor, Department of Industrial Managemnet, Rasht Branch, Islamic Azad University, Rasht,Iran
3 - Assistant Professor, Department of Accounting, Rasht Branch, Islamic Azad University, Rasht, Iran
4 - Assistant Professor, Department of Information Technology Management, Electronic Branch, Islamic Azad University, Tehran, Iran
الکلمات المفتاحية: Credit Scoring, Banking Industry, Criteria, FCM,
ملخص المقالة :
Credit scoring is one of the fundamental concepts in bank industry, used for analyzing and evaluating the customers who request for facilities. Because of its importance to its profitability, especially in the developing countries, this study aims to propose a fuzzy cognitive mapping approach for analyzing the potential criteria in credit scoring of legal clients. This research is applied in terms of purpose and survey in terms of method. This research has been conducted in several steps. In the first step, after reviewing the literature, the important factors for scoring the bank's legal clients were identified and 29 criteria out of them were selected. In the next step, a primal evaluation of these criteria by 16 experts, resulted in the 12 more important criteria (total debt ratio, return on assets, ratio of fixed assets to equity, average customer account, customer capital, the ratio of the deferred amount to current assets, ownership ratio (equity to total assets), amount of received facilities, borrowing capacity, amount of requested facility, type of guarantee and credit risk of the last period) were used in the modeling process. Due to the need for fuzzy logic regarding subjective judgments in cause-effect relationships between criteria, fuzzy cognitive mapping (FCM) method was used to visualize the relationships among these factors. The results show that the amount of requested facilities, customer capital, borrowing capacity and amount of received facilities, ratio of fixed assets to equity, and average customer account are the critical criteria in credit scoring of the legal clients.
1. Aburime, U. T. (2009). Impact of political affiliation on bank profitability in Nigeria, African Journal of Accounting. Economics, Finance and Banking Research, 4(4), 61-75.
2. Asteriou, D., & Spanos, K. (2019). The relationship between financial development and economic growth during the recent crisis: Evidence from the EU. Finance Research. Finance Research Letters, 28, 235-248.
3. Barra, C., & Ruggiero, N. (2021). Do microeconomic and macroeconomic factors influence Italian bank credit risk in different local markets? Evidence from cooperative and non-cooperative banks. Journal of Economics and Business, 19(114),141 – 169.
4. Bekhet, H.A., & Eletter S.F.K. (2014). Credit risk assessment model for Jordanian commercial banks: Neural scoring approach, Review of Development Finance, 4, 20-28.
5. Bülbül, D., Hakenes, H., Lambert, C. (2019). What influences banks’ choice of credit risk management practices?” Theory and evidence. Journal of Financial Stability, 40, 1-14.
6. Ceylan, O. (2021). Time-Varying Risk Aversion and its Macroeconomic and Financial Determinants: A Comparative Analysis in the U.S. and French Financial Markets. Finance Research Letters, 41, 101804.
7. Chamberlain, T.W., Hidayat, S., & Rahman Khokhar, A. (2018). Credit Risk in Islamic and Conventional Banking. International Atlantic Economic Society, 24 (1), 99-100.
8. Delafrooz, N., Homayounfar, M., Taghipour Tamijani, M. (2019). Credit risk management in banks using a hybrid approach. Financial Engineering and Portfolio Management, 10 (38), 94-116.
9. Ehtesham Rasi, R., Karimipour, M., & Azad, M. (2020). Rating the Actual Customers of Banks based on Credit Risk using Multiple Criteria Decision Making and Artificial Intelligence Hyperbolic Regression. International Journal of Finance & Managerial Accounting, 4(16), 51-63.
10. Farre-Danesh, K. & Homayounfar, M. (2015). Ranking the affecting factors in performance evaluation of banking sector based on fuzzy AHP. Universal Journal of Management and Social Sciences 5(4), 9-18
11. Fernandes, G.B., & Artes, R. (2016). Spatial dependence in credit risk and its improvement in credit scoring. European Journal of Operational Research, 249 (2), 517-524.
12. Felix, G., Nápoles, G., Falcon, R., Froelich, W., Vanhoof, K. & Bello, R. (2017). A review on methods and software for fuzzy cognitive maps. Artificial Intelligence Review, 52, 1707-1737.
13. Ferreira, F. A. F., Ferreira, J. J. M., Fernandes, C. I. M. A. S., Meidute- Kavaliauskiene, I., Jalal, M. S. (2017). Enhancing Knowledge and Strategic Planning of Bank Customer Loyalty Using Fuzzy Cognitive Maps. Technological and Economic Development of Economy, 6, 860-876. https://doi.org/10.3846/20294913.2016.1213200
14. Ghane, F., Gilaninia, S., Homayounfar, M. (2016). The effect of cloud computing on effectiveness of customer relation management in electronic banking industry: a case study of eghtesad novin bank. Kuwait Chapter of the Arabian Journal of Business and Management Review 5 (8), 52-105.
15. Kharaghani, M., Homayounfar, M., & Taleghani, M. (2023). Value Chains Analysis: Application of Fuzzy Cognitive Map in Pharmaceutical Industry. Journal of System Management, x, xx-xx.
16. Koford, K., & Tschoegl, A.D. (1997). Problems of Banking Lending in Bulgaria: Information Asymmetry and Institutional learning, Financial Institutions center, The Wharton School University of Pennsylvania, Philadelphia, P. A.
17. Koso, B. (1986), Fuzzy cognitive maps. International Journal of Man-Machine Studies, 24, 65-75.
18. Kozodoi, N., Jacob, J., & Lessmann, S. (2022). Fairness in credit scoring: Assessment, implementation and profit implications. European Journal of Operational Research, 297 (3), 1083-1094.
19. Li, Y., Wang, X., Djehiche, B., & Hu, X. (2020). Credit scoring by incorporating dynamic networked information. European Journal of Operational Research, 286 (3), 1103-1112.
20. Maldonado, S., Peters, G., & Weber, R. (2020). Credit scoring using three-way decisions with probabilistic rough sets. Information Sciences, 507, 700-714.
21. Modjtahedi, A. & Daneshvar, A. (2020). A New Credit Risk System Using Hybrid ELECTRE TRI and NSGA-II Methods, Journal of System Management, 6 (4), 1-25.
22. Mousavi, S.F., Eskandari, N., Abtahi, S.A.R. & Gazri Neishaburi, A. (2023). Providing a model to validate legal clients applying for working capital facilities of a commercial bank. The 5th National Conference and the 2nd International Conference on New Patterns of Business Management in Unstable Conditions, Tehran.
23. Movahedi, M., Homayounfar, M., Fadaei Eshkiki, M., Soufi, M. (2023). Development of a model based on fuzzy cognitive map to analyze the performance of stock exchange firms. Journal of Securities Exchange, 16 (61), 41-60
24. Stylios, C. D., Groumpos, P. P. (2004). Modeling complex systems using fuzzy cognitive maps, IEEE Transactions on Systems, Man and Cybernetics – Part A: Systems and Humans, 34(1), 155–162.
25. Tsiga, Z., Emes, M. & Smith, A. (2017). Implementation of a risk management simulation tool, Procedia Computer Science, 121, 218–223
26. Ullah, S., Wang, Zh., Stokes, P., & Xiao, W. (2019). Risk perceptions and risk management approaches of Chinese overseas investors: An empirical investigation. Research in International Business and Finance, 47, 470-486.
27. Vanhoenshoven, F., Nápoles, G., Froelich, W., Salmeron, J.L. & Vanhoof, K. (2020). Pseudoinverse learning of Fuzzy Cognitive Maps for multivariate time series forecasting. Applied Soft Computing, 95,106461.
28. Vural-Yavas, C. (2020). Corporate risk-taking in developed countries: The influence of economic policy uncertainty and macroeconomic conditions. Journal of Multinational Financial Management, 54 (124), 17-41
29. Waeibrorheem, W., & Sukri, S. (2015). Bank Specific and Macroeconomics Dynamic Determinants of Credit Risk in Islamic Banks and Conventional Banks. International Journal of Economics and Financial Issues, 5(2), 476–481.
30. Wang, G., & Ma, J. (2011). Study of corporate credit risk prediction based on integrating boosting and random subspace. Expert Systems with Applications, 38 (11), 13871-13878.
31. Witzany, J. (2017). Credit Risk Management Pricing, Measurement and Modelling. Springer.
32. Yaman, D., Polat, S. (2009). A fuzzy cognitive map approach for effect-based operations: an illustrative case. Information Sciences, 179(4), 382–403.
33. Yang, D., Xiao, B., Cao, M., & Shen, H. (2024). A new hybrid credit scoring ensemble model with feature enhancement and soft voting weight optimization. Expert Systems with Applications, 238, 122101.
34. Yousofi Tezerjan, M., Safi Samghabadi, A., & Memariani, A. (2021). ARF: A hybrid model for credit scoring in complex systems. Expert Systems with Applications, 185, 2021, 115634.
35. Zaker Nia, Ehsan, Khadem Alizadeh, Amir. (2016). Principles Governing Validation in Credit Risk Management of Islamic Banking from the Perspective of the Quran, 6 (2), 5-34.
36. Zhang, W., Xu, W., Hao, H., & Zhu, D. (2020). Cost-sensitive multiple-instance learning method with dynamic transactional data for personal credit scoring, Expert Systems with Applications, 157, 113489.
37. Zhang, X., Yang, Y., & Zhou, Z. (2018). A novel credit scoring model based on optimized random forest. In 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC) (pp. 60-65).
38. Zhang, W., Yang, D., Zhang, S., Ablanedo-Rosas, J.H., Wu, X. & Lou, Y. (2021). A novel multi-stage ensemble model with enhanced outlier adaptation for credit scoring. Expert Systems with Applications, 165, 113872.
