Improving a Credit Scoring Model by Incorporating Bank Statement Derived Features

In this paper, we investigate the extent to which features derived from bank statements provided by loan applicants, and which are not declared on an application form, can enhance a credit scoring model for a New Zealand lending company. Exploring the potential of such information to improve credit scoring models in this manner has not been studied previously. We construct a baseline model based solely on the existing scoring features obtained from the loan application form, and a second baseline model based solely on the new bank statement derived features. A combined feature model is then created by augmenting the application form features with the new bank statement derived features. Our experimental results show that a combined feature model performs better than both of the two baseline models, and that a number of the bank statement derived features have value in improving the credit scoring model. As is often the case in credit scoring, our target data was highly imbalanced, and Naive Bayes was found to be the best performing classifier, outperforming a number of other classifiers commonly used in credit scoring. Future experimentation with Naive Bayes on other highly imbalanced credit scoring data sets will help to confirm whether the classifier should be more commonly used in the credit scoring context.