Credit-scoring models in the credit-union environment using neural networks and genetic algorithms

Abstract

The purpose of the paper is to investigate the predictive power of feedforward neural networks and genetic algorithms in comparison to traditional techniques such as linear discriminant analysis and logistic regression. A particular advantage offered by the new techniques is that they can capture nonlinear relationships. Also, previous studies and a descriptivedata analysis of the data suggested that classifying loans into three types—namely good, poor, and bad—might be preferable to classifying them into just good and bad loans, and hence a three-way classification was attempted. Our results indicate that the traditional techniques compare very well with the two new techniques studied. Neural networks performed somewhat better than the rest of the methods for classifying the most difficult group, namely poor loans. The fact that the Al-based techniques did not significantly outperform the conventional techniques suggests that perhaps the most appropriate variants of the techniques were not used. However, a post-experiment analysis possibly indicates that the reason for the new techniques not significantly outperforming the traditional techniques was the nonexistence of important consistent nonlinear variables in the data sets examined.