Measuring customer's credit risk in banking industry

Abstract

This paper presents a comparative study of using a linear probability and Logit models to predict credit risk of the customers in some branches of Bank Mellat in Tehran, Iran. The statistical population of this research includes the applicants of the facilities granted by Bank Mellat in Tehran during the year 2008. Each branches of Bank Mellat of Tehran has been considered as a cluster, where a sample has been taken using simple random method. The sample size consists of 176 companies, 109 legal entities are classified as those ones good at settling their accounts, and 67 as those ones tardy in settling their accounts. The financial ratios of these companies have been calculated based on their audited financial statements and by descriptive and analytical methods of two statistical models. The results show that liquidity ratios are not significant factors for the prediction of credit risks and these two models are not significantly different from each other in this term. Moreover, the accuracy values of credit risk prediction of linear and Logit models are 73.7 percent and 80.3 percent, respectively. Therefore, Logit model is more consistent with reality and more appropriate for such a prediction.