Credit scoring using neural and evolutionary techniques

This paper compares the predictive performance of linear discriminant analysis, neural networks, genetic algorithms and decision trees in distinguishing between good and slow payers of bank credit card accounts. Predictive models were built using the evolutionary techniques and the results compared with those gained from the discriminant analysis model published by Crook et al., which uses the same dataset. A range of parameters under the control of the investigator was investigated. We found that the predictive performance of linear discriminant analysis was superior to that of the other three techniques. This is consistent with some studies but inconsistent with others.