Development and Explanation of Bank Customers' Credit System Based on Hybrid Learning Models: A Case Study of Bank Mellat

Document Type : Original Article

Authors

1 Ph.D. Candidate in Financial Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran

2 Associate prof., Department of Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran.

3 Associate Prof, Department of Management, Science and Research Branch, Islamic Azad University, Tehran, Iran.

Abstract

The possibility to renewal of loan contracts in Iran may lead to the identification of fictitious profits by banks and ultimately lead to a banking crisis and disruption of the country's monetary system, so to prevent banks from reaching the stage of this, Measuring customers' credit risk is essential. The aim of this study is to increase the accuracy of customer accreditation using the structure of hybrid models and has been done as a case study on Mellat Bank. In this regard, 14 learning models were compared with each other and their ability to validate customers was determined. Learning models show that based on both accuracy criteria (success rate) and measurement F (harmonic mean between accuracy and recall), the combined learning model (KNN-NN-SVMPSO) - (DL) - (DBSCAN) with an accuracy rate of 99.90 is the highest It has more performance than other basic and hybrid models. Also, using the principal component analysis (PCA), Gini index, interest rate ratio (IGR) and interest rate (IG) methods to calculate the weight of features and their average rank, it was shown that features such as collateral, The type of collateral and the amount of facilities have been the most important features in distinguishing good from bad customers, respectively.

Highlights

  1. Abzari, M., Nazemi, A., & Abdolmanafi, S. (2005). Data Mining and Customer Relationship Management (CRM) in Banks. Paper presented at the Third International Management Conference. https://civilica.com/doc/65975
  2. Addo, P. M., Guegan, D., & Hassani, B. J. R. (2018). Credit risk analysis using machine and deep learning models. 6(2), 38.
  3. Breuel, T., & Shafait, F. (2010). Automlp: Simple, effective, fully automated learning rate and size adjustment. Paper presented at the The Learning Workshop.
  4. Dadmohammadi, D., & Ahmadi, A. (2015). Credit ranking of bank customers with neural network with lateral connections Journal of Development In Monetary and Banking Management, 2(3), 1-28.
  5. Denison, D. G., Holmes, C. C., Mallick, B. K., & Smith, A. F. (2002). Bayesian methods for nonlinear classification and regression (Vol. 386): John Wiley & Sons.
  6. Eberhart, R. C., Shi, Y., & Kennedy, J. (2001). Swarm intelligence: Elsevier.
  7. Ghasemi, A., & Donyayiharis, T. (2016). Credit Risk Measurement In one of the state-owned banks with a Neural Network Approach. Journal of Financial Engineering and Securities Management, 27, 155-181.
  8. Giudici, P., Hadji-Misheva, B., & Spelta, A. (2020). Network based credit risk models. Quality Engineering, 32(2), 199-211.
  9. Haykin, S. (2004). A comprehensive foundation. Neural networks, 2(2004), 41.
  10. Hosmer Jr, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression (Vol. 398): John Wiley & Sons.
  11. Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. Paper presented at the Proceedings of ICNN'95-international conference on neural networks.
  12. Khansari, R., & Fallahshams, M. (2010). Appraising the Use of KMV Model in Predicting Default of Companies Listed in Tehran Stock Exchange %J Financial Research Journal. 11(28), -.
  13. Mehrara, M., & Sadeghian, S. (2008). Determining the optimal loan composition in economic sectors: (Saman Bank case study). Financial Economics, 2(5), 116-134.
  14. Misman, F. N., & Bhatti, M. I. (2020). The Determinants of Credit Risk: An Evidence from ASEAN and GCC Islamic Banks. 13(5), 89.
  15. mohamadi, T., shakeri, A., Eskandari, F., & Karimi, D. (2017). Factors Shaping the Non-performing Loans in Iranian Banking System: A Case Study, Majlis and Rahbord. 24(89), 269-300.
  16. Nilchi, M., Moghadam, K., SadrAbadi, A., & Farhadian, A. (2019). Predicting the Credit Risk of Loans Using Data Mining Tools. Journal of Monetary and Banking research, 11(38).(in persian).
  17. Roussopoulos, N., Kelley, S., & Vincent, F. (1995). Nearest neighbor queries. Paper presented at the Proceedings of the 1995 ACM SIGMOD international conference on Management of data.
  18. Tehrani, R., & Fallahshamsi, M. (2005). Designing and explaining the credit risk model in the country's banking system Article. Social Sciences and Humanities (Shiraz University), 43, 45-60.
  19. Thomas, L. C. J. I. j. o. f. (2000). A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers. 16(2), 149-172.
  20. Tisan, A., Cirstea, M. J. M., & Simulation, C. i. (2013). SOM neural network design–A new Simulink library based approach targeting FPGA implementation. 91, 134-149.
  21. Tsai, C.-F., & Chen, M.-L. J. A. s. c. (2010). Credit rating by hybrid machine learning techniques. 10(2), 374-380.
  22. Umar, M., Ji, X., Mirza, N., & Naqvi, B. (2021). Carbon neutrality, bank lending, and credit risk: Evidence from the Eurozone. Journal of Environmental Management, 296, 113156.
  23. Xi-yu, F. X.-f. L. (2006). New Evolution and Development Preview of Decision Tree in Data Mining [J]. Information Technology Informatization, 3.
  24. Xin, J., & Xiaofeng, H. J. J. o. C. I. T. (2012). A quantum-PSO-based
  25. Zopounidis, C., & Doumpos, M. J. T. (2013). Multicriteria decision systems for financial problems. 21(2), 241-261.

Keywords

References

  1. Abzari, M., Nazemi, A., & Abdolmanafi, S. (2005). Data Mining and Customer Relationship Management (CRM) in Banks. Paper presented at the Third International Management Conference. https://civilica.com/doc/65975
  2. Addo, P. M., Guegan, D., & Hassani, B. J. R. (2018). Credit risk analysis using machine and deep learning models. 6(2), 38.
  3. Breuel, T., & Shafait, F. (2010). Automlp: Simple, effective, fully automated learning rate and size adjustment. Paper presented at the The Learning Workshop.
  4. Dadmohammadi, D., & Ahmadi, A. (2015). Credit ranking of bank customers with neural network with lateral connections Journal of Development In Monetary and Banking Management, 2(3), 1-28.
  5. Denison, D. G., Holmes, C. C., Mallick, B. K., & Smith, A. F. (2002). Bayesian methods for nonlinear classification and regression (Vol. 386): John Wiley & Sons.
  6. Eberhart, R. C., Shi, Y., & Kennedy, J. (2001). Swarm intelligence: Elsevier.
  7. Ghasemi, A., & Donyayiharis, T. (2016). Credit Risk Measurement In one of the state-owned banks with a Neural Network Approach. Journal of Financial Engineering and Securities Management, 27, 155-181.
  8. Giudici, P., Hadji-Misheva, B., & Spelta, A. (2020). Network based credit risk models. Quality Engineering, 32(2), 199-211.
  9. Haykin, S. (2004). A comprehensive foundation. Neural networks, 2(2004), 41.
  10. Hosmer Jr, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression (Vol. 398): John Wiley & Sons.
  11. Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. Paper presented at the Proceedings of ICNN'95-international conference on neural networks.
  12. Khansari, R., & Fallahshams, M. (2010). Appraising the Use of KMV Model in Predicting Default of Companies Listed in Tehran Stock Exchange %J Financial Research Journal. 11(28), -.
  13. Mehrara, M., & Sadeghian, S. (2008). Determining the optimal loan composition in economic sectors: (Saman Bank case study). Financial Economics, 2(5), 116-134.
  14. Misman, F. N., & Bhatti, M. I. (2020). The Determinants of Credit Risk: An Evidence from ASEAN and GCC Islamic Banks. 13(5), 89.
  15. mohamadi, T., shakeri, A., Eskandari, F., & Karimi, D. (2017). Factors Shaping the Non-performing Loans in Iranian Banking System: A Case Study, Majlis and Rahbord. 24(89), 269-300.
  16. Nilchi, M., Moghadam, K., SadrAbadi, A., & Farhadian, A. (2019). Predicting the Credit Risk of Loans Using Data Mining Tools. Journal of Monetary and Banking research, 11(38).(in persian).
  17. Roussopoulos, N., Kelley, S., & Vincent, F. (1995). Nearest neighbor queries. Paper presented at the Proceedings of the 1995 ACM SIGMOD international conference on Management of data.
  18. Tehrani, R., & Fallahshamsi, M. (2005). Designing and explaining the credit risk model in the country's banking system Article. Social Sciences and Humanities (Shiraz University), 43, 45-60.
  19. Thomas, L. C. J. I. j. o. f. (2000). A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers. 16(2), 149-172.
  20. Tisan, A., Cirstea, M. J. M., & Simulation, C. i. (2013). SOM neural network design–A new Simulink library based approach targeting FPGA implementation. 91, 134-149.
  21. Tsai, C.-F., & Chen, M.-L. J. A. s. c. (2010). Credit rating by hybrid machine learning techniques. 10(2), 374-380.
  22. Umar, M., Ji, X., Mirza, N., & Naqvi, B. (2021). Carbon neutrality, bank lending, and credit risk: Evidence from the Eurozone. Journal of Environmental Management, 296, 113156.
  23. Xi-yu, F. X.-f. L. (2006). New Evolution and Development Preview of Decision Tree in Data Mining [J]. Information Technology Informatization, 3.
  24. Xin, J., & Xiaofeng, H. J. J. o. C. I. T. (2012). A quantum-PSO-based
  25. Zopounidis, C., & Doumpos, M. J. T. (2013). Multicriteria decision systems for financial problems. 21(2), 241-261.
Financial Management Perspective
Volume 12, Issue 37
April 2022
Pages 69-94

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