An Evaluation of Iranian Banking System Credit Risk: Neural Network and Logistic Regression

Abstract

The current study seeks to provide a new solution for evaluation of banking system customers risk by<br><br>integrating different scientific methodology. Evaluation of banking system customers risk in Iranian<br><br>banks relies on experts judgment and fingertip rule. This type of evaluation resulted in high rate of<br><br>postponed claims; therefore, designing new intelligent model for credit risk evaluation will be helpful,<br><br>thus in this paper, we formulated an intelligent model by neural network and logistic regression that<br><br>evaluated all individual customers credit risk without prejudice and discrimination. The result revealed<br><br>that neural network and logistic regression have the same ability in predicting customer credit risk.<br><br>Their ability in customer credit risk correct evaluation was nearly 79.50%. We suggested that both<br><br>models could be used by all financial system as consultant model for customer credit risk prediction.<br><br>The study also involved only one banking system credit customers, which concerns just Tehran city<br><br>customers and its sample includes only individual customers, thus cannot be for institutional<br><br>customers. Offering a case study, this paper presents a guide for banking system to predict any<br><br>customer credit risk and regulate any customer loan in the light of customer risk that was extracted by<br><br>neural network, and logistic regression employed different scientific methodologies in their service<br><br>quality development efforts. Intending to offer scientific approaches to risk evaluation as a tool of<br><br>customer credit risk assessment in banking system loan allocation procedures, this paper tries to<br><br>bridge the current gap between academicians and practitioners; adds to the relatively limited<br><br>theoretical literature.