Measuring Credit Risk of Bank Customers Using Artificial Neural Network

Mohsen Nazari, Mojtaba Alidadi

Abstract


In many studies, the relationship between development of financial markets and economic growth has been proved. Credit risk is one of problems which banks are faced with it while doing their tasks. Credit risk means the probability of non-repayment of bank financial facilities granted to investors. If the credit risk decreases, banks will be more successful in performing their duties and have greater effect on economic growth of the country. Credit rating of customers and identifying good and bad customers, help banks lend to their good payers and hereby, they reduce probability of non-repayment. This paper aims to identify classification criteria for good customers and bad customers in Iranian banks. This study can classified in applied studies group and the research strategy is descriptive. Artificial neural network technique is used for financial facilities applicants' credit risk measurement and the calculations have been done by using SPSS and MATLAB software. Number of samples was 497 and 18 variables were used to identify good customers from bad customers. The results showed that, individual loan frequency and amount of loan had most important effect and also status of customer’s bank account, history of customer relationship with bank and received services had least important effect in identifying classification criteria of good and bad customers. The major contribution of this paper is specifying the most important determinants for rating of customers in Iran’s banking sector.


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References


Angelini, E., di Tollo, G., & Roli, A. (2008). A neural network approach for credit risk evaluation. The Quarterly Review of Economics and Finance, 48(4), 733-755. http://dx.doi.org/10.1016/j.qref.2007.04.001

Atiya, A. F. (2001). Bankruptcy prediction for credit risk using neural networks: A survey and new results. IEEE Transactions on Neural Networks, 12(4), 929-935. http://dx.doi.org/10.1109/72.935101

Curry, B., Morgan, P., & Silver, M. (2002). Neural networks and non-linear statistical methods: an application to the modelling of price–quality relationships. Computers & Operations Research, 29(8), 951-969. http://dx.doi.org/10.1016/S0305-0548(00)00096-4

Eletter, S. F., & Yaseen, S. G. (2010). Applying Neural Networks for Loan Decisions in the Jordanian Commercial Banking System. 10(1), 209-214.

Ghodselahi, A., & Amirmadhi, A. (2011). Application of Artificial Intelligence Techniques for Credit Risk Evaluation. International Journal of Modeling and Optimization, 1(3), 243-249. http://dx.doi.org/10.7763/IJMO.2011.V1.43

Gouvêa, M. A., & Gonçalves, E. B. (2007). Credit Risk Analysis Applying Logistic Regression, Neural Networks and Genetic Algorithms Models. Paper presented at the Production and Operations Management Society (POMS), Dallas, Texas, U.S.A.

Hall, M. J. B., Muljawan, D., Suprayogi, & Moorena, L. (2009). Using the artificial neural network to assess bank credit risk: a case study of Indonesia. Applied Financial Economics, 19(22), 1825-1846. http://dx.doi.org/10.1080/09603100903018760

Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Networks, 2(5), 359-366. http://dx.doi.org/10.1016/0893-6080(89)90020-8

Khashman, A. (2010). Neural networks for credit risk evaluation: Investigation of different neural models and learning schemes. Expert Syst. Appl., 37(9), 6233-6239. http://dx.doi.org/10.1016/j.eswa.2010.02.101

Matoussi, H., & Abdelmoula, A. (2009). Using a Neural Network-Based Methodology for Credit–Risk Evaluation of a Tunisian Bank. Middle Eastern Finance and Economics(4), 117-140.

Pacelli, V., & Azzollini, M. (2011). An Artificial Neural Network Approach for Credit Risk Management. Journal of Intelligent Learning Systems and Applications, 3(2), 103-112. http://dx.doi.org/10.4236/jilsa.2011.32012

Salehi, M., & Mansoury, A. (2011). An evaluation of Iranian banking system credit risk: Neural network and logistic regression approach. International Journal of the Physical Sciences, 6(25), 6082-6090. http://dx.doi.org/10.5897/IJPS10.640

Steiner, M. T. A., Neto, P. J. S., Soma, N. Y., Shimizu, T., & Nievola, J. C. (2006). Using Neural Network Rule Extraction for Credit-Risk Evaluation. International Journal of Computer Science and Network Security, 6(5A), 6-16.

Vasconcelos, G. C., Adeodato, P. J. L., & Monteiro, D. S. M. P. (1999, July 20-22). A Neural Network Based Solution for the Credit Risk Assessment Problem. Paper presented at the IV Brazilian Conference on Neural Networks, São José dos Campos.

Yu, L., Wang, S., & Lai, K. K. (2008). Credit risk assessment with a multistage neural network ensemble learning approach. Expert Systems with Applications, 34(2), 1434-1444. http://dx.doi.org/10.1016/j.eswa.2007.01.009




DOI: http://dx.doi.org/10.5296/jmr.v5i2.2899

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