Comparison of the Performances of Classical Models and Artificial Intelligence in Predicting Bank Customers' Credit Status

Currently, in the banking system, defaults in the repayment of loans have become one of the biggest problems, and banks and financial institutions have faced many problems such as the increase in the volume of outstanding receivables due to the lack of an appropriate system for allocating facilities. Considering the importance of credit risks, commercial banks used to apply judgment methods for determining those risks. However, the use of these methods was not efficient enough due to limited human abilities and, at the same time, various factors affecting credit risks in contrast to statistical methods as well as artificial intelligence methods. For this reason, this article measures the efficiency of logistic regression models and artificial neural networks in detection of bank customers’ credit status in the period of 2009-2013. The results indicated that the total accuracy rates of the Artificial Neural Network model and the Logistic Regression model were 87% and 77.2% respectively, and the error types I and II were reduced significantly in the neural network. According to the results, statistical models cannot be expected to properly evaluate the credit risk of customers with classical assumptions such as the linear relationship between variables. Therefore, application and integration of artificial intelligence techniques is strongly recommended in this regard.