Using the Hybrid Model for Credit Scoring (Case Study: Credit Clients of microloans, Bank Refah-Kargeran of Zanjan, Iran)
Subject Areas : Environmental ManagementAbdollah Nazari 1 , Mohammadreza Mehregan 2 , Reza Tehrani 3
1 - Department of Management, Alborz College, University of Tehran, Tehran, Iran
2 - Faculty of Management, University of Tehran, Tehran, Iran
3 - Faculty of Management, University of Tehran, Tehran, Iran.
Keywords: Data mining, Clustering, Credit Scoring, UTADIS,
Abstract :
In any country, commercial banks lay the groundwork for economic growth by collecting national resources and capitals and allocating them to different economic sectors. Optimal allocation of resources is especially important in achieving this goal. Banks with an effective and dynamic system of customer assessment can efficiently allocate their resources to customers regardless of their geographic area. Following[M1] a linear programming optimization approach, this research employs the UTilités Additives DIScriminantes (UTADIS) model for credit scoring of bank customers. The advantages of the proposed technique are high flexibility, mutual interaction with decision makers, and the ability to update under various macroeconomic conditions. The chosen environment is a branch of Bank Refah Kargaran, one of the popular banks in Iran. According to the experimental results, the proposed technique demonstrates high effectiveness. Also, the results indicate that the initial credit score and age of the applicants are the most influential factors for credit scoring of customers.
Abdou, H. A. (2009). Genetic programming for credit scoring: The case of Egyptian public sector banks. Expert Systems with Applications, 36(9), 11402-11417.
Alborzi, M., Khanbabaei, M., & Mohammad Pourzarandi, M. E. (2012). Using Clustering and Genetic Algorithm Techniques in Optimizing Decision Trees for Credit Scoring of Bank Customers. Scientific Journal Management System, 1.1(1), 15-34.
Armeshi, M. (2011). Identification of variables that affect credit risk: A case of the customers of Saman Bank. (Master’s Thesis), Payame Noor University, Behshahr, Mazandaran.
Bequé, A., & Lessmann, S. (2017). Extreme learning machines for credit scoring: An empirical evaluation. Expert Systems with Applications, 86, 42-53.
Danenas, P., & Garsva, G. (2015). Selection of Support Vector Machines based classifiers for credit risk domain. Expert Systems with Applications, 42(6), 3194-3204.
Dong, G., Lai, K. K., & Yen, J. (2010). Credit scorecard based on logistic regression with random coefficients. Procedia Computer Science, 1(1), 2463-2468.
Doumpos, M., & Figueira, J. R. (2019). A multicriteria outranking approach for modeling corporate credit ratings: An application of the Electre Tri-nC method. Omega, 82, 166-180.
Doumpos, M., & Zopounidis, C. (2002a). Multi–criteria Classification Methods in Financial and Banking Decisions. International Transactions in Operational Research, 9(5), 567-581.
Doumpos, M., & Zopounidis, C. (2002b). Multicriteria Decision Aid Classification Methods: Springer US.
Esmaelian, M., Shahmoradi, H., & Nemati, F. (2017). P-UTADIS: A Multi Criteria Classification Method. In F. Nassiri-Mofakham (Ed.), Current and Future Developments in Artificial Intelligence (Vol. 1).
Faezy Razi, F., & Shadloo, N. (2017). A Hybrid Grey based Two Steps Clustering and Firefly Algorithm for Portfolio Selection. Journal of Optimization in Industrial Engineering, 10(22), 49-59.
Figueira, J., Mousseau, V., & Roy, B. (2005). Electre Methods Multiple Criteria Decision Analysis: State of the Art Surveys (pp. 133-153). New York, NY: Springer New York.
Gaganis, C., Pasiouras, F., Tanna, S., & Zopounidis, C. (2008). Binary choice models for external auditors decisions in Asian banks. Operational Research, 8(2), 123-139.
He, H., Zhang, W., & Zhang, S. (2018). A novel ensemble method for credit scoring: Adaption of different imbalance ratios. Expert Systems with Applications, 98, 105-117.
Jones, S., Johnstone, D., & Wilson, R. (2015). An empirical evaluation of the performance of binary classifiers in the prediction of credit ratings changes. Journal of Banking & Finance, 56, 72-85.
Kamali, A. M. (2011). Factors affecting customers’ credit scores and a model for ranking them: A case of Sina Bank. Faculty of Management and Accounting, Islamic Azad University.
Kazemi, A., & Babaei, M. E. (2011). Modelling Customer Attraction Prediction in Customer Relation Management using Decision Tree: A Data Mining Approach. Journal of Optimization in Industrial Engineering, Volume 4(9), 37-45.
Louzada, F., Ferreira-Silva, P. H., & Diniz, C. A. R. (2012). On the impact of disproportional samples in credit scoring models: An application to a Brazilian bank data. Expert Systems with Applications, 39(9), 8071-8078.
Luo, C., Wu, D., & Wu, D. (2017). A deep learning approach for credit scoring using credit default swaps. Engineering Applications of Artificial Intelligence, 65, 465-470.
Mousavi, R., & Gholipour, E. (2009). Ranking credit scoring criteria using the Delphi method. Proceedings of the 1st International Conference on Marketing Banking Services, 842-853.
Otten, R., & Bams, D. (2002). European mutual fund performance. European financial management, 8(1), 75-101.
Parvaneh, A., Abbasimehr, H., & Tarokh, M. J. (2012). Integrating AHP and data mining for effective retailer segmentation based on retailer lifetime value. Journal of Optimization in Industrial Engineering, 5(11), 25-31.
Pendaraki, K., Zopounidis, C., & Doumpos, M. (2005). On the construction of mutual fund portfolios: A multicriteria methodology and an application to the Greek market of equity mutual funds. European Journal of Operational Research, 163(2), 462-481.
Spathis, C., Doumpos, M., & Zopounidis, C. (2003). Using client performance measures to identify pre-engagement factors associated with qualified audit reports in Greece. International Journal of Accounting, 38(3), 267-284.
Vukovic, S., Delibasic, B., Uzelac, A., & Suknovic, M. (2012). A case-based reasoning model that uses preference theory functions for credit scoring. Expert Systems with Applications, 39(9), 8389-8395.
Xia, Y., Liu, C., Da, B., & Xie, F. (2018). A novel heterogeneous ensemble credit scoring model based on bstacking approach. Expert Systems with Applications, 93, 182-199.
Zheng, J., Cailloux, O., & Mousseau, V. (2011). Constrained Multicriteria Sorting Method Applied to Portfolio Selection. Proceedings of the International Conference on Algorithmic Decision Theory, 331-343.
Zimmermann, H. J. (2001). Fuzzy Set Theory—and Its Applications: Springer Netherlands.
Zopounidis, C., & Doumpos, M. (2002). Multi‐criteria decision aid in financial decision making: methodologies and literature review. Journal of Multi‐Criteria Decision Analysis, 11(4‐5), 167-186.