One of the most important issues on revenue management in banking industries is the assignment of loan to customers. In fact, a big portion of banks' revenue is from loan assignment and choosing appropriate customers for loan assignment not only reduces the financial risk but also it can increase the revenue. In this study, we perform an empirical study to find out whether customers' financial statements could provide enough information about customers for loan assignment. The other objective of this study is to find out whether banks' officials could understand about the details of customers' financial statements. Finally, we want to find out whether there is any relationship between unpaid loans and customers' credit rating. The present study is executed on an Iranian bank by distributing a questionnaire analyzing the results. The results indicate that there are some strong evidence that financial statement could help bank official determine customers' credit rating. The survey also concludes that highly education and experienced employees are the best people for devoting best credit rating for customers.
DOI: j.msl.2011.08.005 Keywords: Banking industry ,Loan assignment ,Customer rating ,Financial transcript ,Revenue management How to cite this paper: Najafi, H & Ahmadkhani, A. (2012). An empirical study on the relationship between customers' credit rating and their financial statements for loan assignment.Management Science Letters, 2(1), 301-306.
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.
Bannierand, C.E., & Hirsch, C.W. (2010).The economic function of credit rating agencies – What does the watchlist tell us?. Journal of Banking & Finance, 34(12), 3037-3049. Becker, B., & Milbourn, T. (2011). How did increased competition affect credit ratings?. Journal of Financial Economics, 101(3), 493-514. Burak Emel, A., Oral, M., Reisman, A., & Yolalan, R. (2003). A credit scoring approach for the commercial banking sector. Socio-Economic Planning Sciences. 37(2), 103-123. Curry, T.J., Fissel, G.S., & Ramirez, C.D. (2008). The impact of bank supervision on loan growth. The North American Journal of Economics and Finance, 19(2), 113-134. Carey, M., & Hrycay, M. (2001). Parameterizing credit risk models with rating data. Journal of Banking & Finance, 25(1), 197-270. Grunert, J., Norden, L., & Weber, M. (2005). The role of non-financial factors in internal credit ratings. Journal of Banking & Finance, 29(2), 509-531. Jacobson, T., Lindé, J., & Roszbach, K. (2006). Internal ratings systems, implied credit risk and the consistency of banks’ risk classification policies. Journal of Banking & Finance, 30(7), 1899-1926. Treacy, W. F., & Carey, M. (2000). Credit risk rating systems at large US banks. Journal of Banking & Finance, 24(1-2), 167-201. Tsai, C.F., & Chen, M.L. (2010). Credit rating by hybrid machine learning techniques. Applied Soft Computing, 10(2), 374-380. Stefanescu, C., Tunaru, R., Turnbull, S. (2009).The credit rating process and estimation of transition probabilities: A Bayesian approach. Journal of Empirical Finance, 16(2), 216-234. Thomas, H., & Wang, Z. (2004). The integration of bank syndicated loan and junk bond markets. Journal of Banking & Finance, 28(2), 299-329. |
![]() |
® 2013 GrowingScience.Com