Credit Scoring Model for Iranian Banking Customers and Forecasting Creditworthiness of Borrowers

Abstract

Credit risk is one of important challenges facing banks and credit institutions in all economic systems. Accordingly, much research has focused on credit scoring of bank customers and to predict and classify solvent customers and insolvent customers. In this line, the present study has attempted to identify and employ financial ratios affecting the creditworthiness of banking customers using discriminant analysis and logistic regression to determine the reliability of different credit scoring models in predicting creditworthiness of banking customers. To do so, customers' credit files in one of the branches of commercial Iranian banks in Province were investigated and totally 54 creditable firms and 46 non-creditable firms were identified. The creditworthiness of these firms was determined through discriminant analysis and logic regression using financial information obtained from the sample firms from 2006 to 2011. The results of the study indicated that the two fitted models are reasonable reliable in predicting the creditworthiness of banking customers. However, the logic regression model had a higher discrimination power than the discriminant analysis model.