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.
[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.
[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.
@article{"International Journal of Business, Human and Social Sciences:57033", author = "Isa Nakhai Kamal Abadi and Esmaeel Saberi and Ehsan Mirjafari", title = "Credit Risk Management and Analysis in an Iranian Bank", abstract = "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.", keywords = "Financial Ratios; Spearman Test; Bank OperationsRisk", volume = "5", number = "6", pages = "859-5", }