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Title

THE DESIGN AND EXPLANATION OF CREDIT RISK MODELING FOR IRANIAN BANKING SYSTEM

Pages

  45-60

Abstract

 Designing and establishing CREDIT RISK predicted models in Iranian banking system has a profound impact on asset allocations productivity. This paper tries to give an analysis of efficiency in related models such as LINEAR PROBABILITY MODEL, LOGIT REGRESSION and ARTIFICIAL NEURAL NETWORKS. Predicted variables are financial ratios of borrowers that via statistical models confirmed significant interrelation between credit. risk and those ratios. Using financial and credit information, CREDIT RISK models were designed and tested for 316 legal entities as main bank customers. Results showed that interrelation between variables was not linear; exponential and sigmoid are best functions for CREDIT RISK predictions. ARTIFICIAL NEURAL NETWORKS and logit models have the most efficiency for risk forecasting.

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References

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APA: Copy

TEHRANI, REZA, & FALLAH SHAMS, M.. (2005). THE DESIGN AND EXPLANATION OF CREDIT RISK MODELING FOR IRANIAN BANKING SYSTEM. JOURNAL OF SOCIAL SCIENCES AND HUMANITIES OF SHIRAZ UNIVERSITY, 22(2 (43)), 45-60. SID. https://sid.ir/paper/13346/en

Vancouver: Copy

TEHRANI REZA, FALLAH SHAMS M.. THE DESIGN AND EXPLANATION OF CREDIT RISK MODELING FOR IRANIAN BANKING SYSTEM. JOURNAL OF SOCIAL SCIENCES AND HUMANITIES OF SHIRAZ UNIVERSITY[Internet]. 2005;22(2 (43)):45-60. Available from: https://sid.ir/paper/13346/en

IEEE: Copy

REZA TEHRANI, and M. FALLAH SHAMS, “THE DESIGN AND EXPLANATION OF CREDIT RISK MODELING FOR IRANIAN BANKING SYSTEM,” JOURNAL OF SOCIAL SCIENCES AND HUMANITIES OF SHIRAZ UNIVERSITY, vol. 22, no. 2 (43), pp. 45–60, 2005, [Online]. Available: https://sid.ir/paper/13346/en

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