A Comparison of the Artificial Neural Network with Classical Methods in Corporate Credit Scoring

Abstract

The failure of banks to correctly analyze the credit worthiness of their customers has devastating consequences. Therefore, the importance of credit scoring in the banking sector has become a major field of research in recent years. There are some methods such as logistic regression, linear regression, discriminant analysis and artificial neural networks for credit scoring. The subject of this research is to evaluate the performance of machine learning and logistic regression models on credit scoring by comparison. In this study, it is aimed to develop a scorecard model in which banks can be exposed to a minimum level of credit risk by comparing the logistic regression and artificial neural network methods which are two of these methods. Although there are studies on the comparison of credit scoring models in the literature, the studies have been conducted through retail portfolios and a sample that covers a maximum of 4 years. Unlike the studies in the literature, this research was conducted through corporate firms and a larger sample than the studies in the literature. The result of the study indicated that artificial neural networks which have higher success than logistic regression on the development sample, saw lower success on the out of sample data. Thus, while artificial neural networks show higher performance, it is concluded that logistic regression provides more consistent results, and it is thought that artificial neural networks can produce more consistent results by optimization of the iteration processes.