Credit Risk Management and Analysis in an Iranian Bank

While financial institutions have faced difficulties over the years for a multitude of reasons, the major cause of serious banking problems continues to be directly related to lax credit standards for borrowers and counterparties, poor portfolio risk management, or a lack of attention to changes in economic or other circumstances that can lead to a deterioration in the credit standing of a bank's counterparties. Credit risk is most simply defined as the potential that a bank borrower or counterparty will fail to meet its obligations in accordance with agreed terms. The goal of credit risk management is to maximize a bank's risk-adjusted rate of return by maintaining credit risk exposure within acceptable parameters. Banks need to manage the credit risk inherent in the entire portfolio as well as the risk in individual credits or transactions. Banks should also consider the relationships between credit risk and other risks. The effective management of credit risk is a critical component of a comprehensive approach to risk management and essential to the long-term success of any banking organization. In this research we also study the relationship between credit risk indices and borrower-s timely payback in Karafarin bank.

Authors:

• Isa Nakhai Kamal Abadi
• Esmaeel Saberi
• Ehsan Mirjafari

Keywords:

• Financial Ratios; Spearman Test; Bank OperationsRisk

References:

[1] Angelini, E., Tollo, G. D., & Roli, A. (2008). A neural network approach
for credit risk evaluation. The Quarterly Review of Economics and
Finance, 48(4), 733-755.
[2] Chen, C. Y., Yu, Y. C., & Wu, B. S. (2005). To corporate governance
and credit information construction bank credit financial crisis early
warning model. Taiwan Bank Quarterly, 1-26.
[3] Altman, E. I. (2006). Modeling credit risk for SMEs: Evidence from the
US market. In Proceedings of the international conference C.R.E.D.I.T.
2006 risk in small business lending. 25-26 September, Venice, Italy.
[4] Dimitras, A. I., Zanakis, S. H., & Zopounidis, C. (1998). A survey of
business failures with an emphasis on prediction methods and industrial
applications. European Journal of Operational Research, 90, 487-513.
[5] Abdou, H., Pointon, J., & Elmasry, A. (2008). Neural nets versus
conventional techniques in credit scoring in Egyptian banking. Expert
Systems and Applications, 35(3), 1275-1292.
[6] Chen, C. Y., Yu, Y. C., & Wu, B. S. (2005). To corporate governance
and credit information construction bank credit financial crisis early
warning model. Taiwan Bank Quarterly, 1-26.
[7] Claessens, S., Djankov, S., & Lang, L. H. P. (1999). Who controls East
Asian corporations? Policy Research Working Paper No. 2054, World
Bank, Washington, DC.
[8] La Porta, R., Lopez-de-Silanes, F., & Shleifer, A. (1999). Corporate
ownership around the world. Journal of Finance, 54, 471-517.
[9] Morck, R., Shleifer, A., & Vishny, R. W. (1988). Management
ownership and market valuation: An empirical analysis. Journal of
Financial Economics, 20, 293-316.
[10] Lee, T. S., Chiu, C. C., & Lu, C. J. (2002). Credit scoring using the
hybrid neural discriminant technique. Expert Systems with Application,
23(3), 245-254.
[11] Hennie van Greuning. Soja Brajovic bratanovie, Analysing Banking
Risk, The world Bank. Washington, D. C. 1999
[12] Sinkey, Jr. Joseph F., Commercial Bank Financial Management , 4th
Edition: Mocmillan, 1998, p. 816.
[13] Bernstein, A. L. & Wild J.J. Financial Statement Analysis. Theory,
Application and Interpretation, Irwin- Mc- Graw - Hill Boston, 1998, p.
168- 174.
[14] Altman, Edward, I. , predicting financial Distres of companies, Revisting
the Z- score and Zeta models, july 2000, p. 189.
[15] Edward, L. Atman, "Financial Ratios Discrininant Analysis & Prediction
of Corporate Bankruptcy" Journal of Finance, 22 Sept, 1968, p. 109.
[16] Anderson, T. W., An Intradaction to Multivariate Statistical Analysis
2nd Edition, 1984 p. 182,
[17] Subhash, Applied Multivariate Techniques, John Wiley & Sons, Inc,
1999, p. 274.