Credit Risk Assessment Utilizing Data Reduction Technique for Individual Loaning in Financial Institutes (Case Study: Tejarat Bank, Rasht, Iran)
American Journal of Theoretical and Applied Business
Volume 3, Issue 1, March 2017, Pages: 11-17
Received: Mar. 16, 2017;
Accepted: Apr. 25, 2017;
Published: May 30, 2017
Views 1885 Downloads 81
Abbas Mahmoudabadi, Department of Industrial Engineering, Mehr Astan University, Astaneh-Ashrafieh, Guilan, Iran
Matin Mehrshad, Department of Industrial Engineering, Mehr Astan University, Astaneh-Ashrafieh, Guilan, Iran
Mohammad Reza Aminnaseri, Department of Industrial Engineering, Tarbiat Modares University, Tehran, Iran
Because of the nature of the financial and economic activities and they are practically accompanied with a degree of risk., banks are usually dealing with many risks, including operational, marketing, interest rate, etc. Since, credit risk has significant effects on financial banks activities in terms of loaning profits, the risk of repayment individual loans has been investigated in this research work. Two well-known regression models of Probit and Logistic have been developed based on nine extracted factors which have been investigated during the offering of loans according to the possibility of late or non-repayment. In order to minimize inter-correlation and extracting high-independency factors, the statistical technique of Principal Component Analysis (PCA), categorized as a data reduction technique, has been utilized and three factors out of nine have been omitted. One of Tejarat bank branches in the Iranian Northern Province of Guilan has been selected as case study to gather experimental data for assessing the credit risk of individual bank investors. The results of model validation revealed that the implementation of PCA method can improve the accuracy of models’ outputs and Probit regression model has better results rather than Logit one.
Mohammad Reza Aminnaseri,
Credit Risk Assessment Utilizing Data Reduction Technique for Individual Loaning in Financial Institutes (Case Study: Tejarat Bank, Rasht, Iran), American Journal of Theoretical and Applied Business.
Vol. 3, No. 1,
2017, pp. 11-17.
Ebrahim, H. Kazem, Y. Nader, M. Reza, KH., Effects of Risk Parameters (Credit, Operational, Liquidity and Market Risk) on Banking System Efficiency (Studying 15 Top Banks in Iran), Iranian Economic Review, (17). 2013.
Soheib, I, The Determinants of Credit Risk in Commercial Banks of Pakistan, Journal of Poverty, Investment and Development, (25). 2422-846X, 2016.
Mandala N., Badra C., Rian F., Assessing Credit Risk: an Application of Data Mining in a Rural Bank, Procedia Economics and Finance, (4). 406- 412, 2012.
Gang, D., Kin, K., and Jerome, Y., Credit scorecard based on logistic regression with random coefficients, Procedia Computer Science, (1), 2463–2468, 2012.
Jacobson T., Kasper R., Bank lending policy, credit scoring and value-at-risk, Journal of Banking & Finance, (27). 615– 633, 2003.
Che, Z. H., Wang, H. S., and Chih-ling, Ch., A fuzzy AHP and DEA approach for making bank loan decisions for small and medium enterprises in Taiwan, Expert Systems with Applications, (37), 7189–7199, 2010.
Baon, Y., Ling, X., Hai, J., and Jing, X., An early warning system for loan risk assessment using artificial neural networks, knowledge-Based Systems, (14), 303-306, 2001.
Kent, E., Sara, J., Jessica, L., and Angelika, L., Modeling firm specific internationalization risk: An application to banks’ risk assessment in lending to firms that do international business, International Business Review, (23). 1074- 1085, 2014.
Jae, H., and Young, L., A practical approach to credit scoring, Expert Systems with Applications, (35). 1762– 1770, 2008.
Johnson and, R., Wichern, D., Applied multivariate statistical analysis, 3rd Edition, Prentice-Hall Inc., Englewood Cliffs, SA, 590, 1982.
Caliendo, C., and Parisi, A., Principal component analysis applied to crash data on multilane roads, Third international SIIV Congress, Bari, Italy, 20-22 September, ANCONA SIIV Vol. 1, Page 1-7, 2005.
Hutcheson, G., and Nick, S., The multivariate social scientist: Introductory statistics using generalized linear models, Thousand Oaks, CA: Sage Publications, 1999.
Singha, K., Malika, A., Mohana, D., and Sinhab, S., Multivariate statistical techniques for the evaluation of spatial and temporal variations in water quality of Gomti River (India) a case study, Water Research, Vol. 38, pp. 3980–3992, 2004.
Hsiao, CH., the Econometric Of Panel Data, Handbook of Theory and Applications, Vol. 28, pp. 223-241, 1993.
Cattel, R., Thescree test for the number of the factor, Multivariate Behavioral Research, Vol. 1, April, pp 245-276, 1996.
Horowitz, J., and Savin, N., Binary Response Models: logits, probits and sami parametrics, Journal of Economic Perspectives, Vol. 15, NO 4, pp. 43-56, 2001.