A Credit Scoring Model Based on Bayesian Network and Mutual Information

Credit scoring profiles the client relationships of empirical attributes (variables) and leverages a scoring model to draw client's credibility. However, empirical attributes often contains a certain degree of uncertainty and requires feature selection. Bayesian network (BN) is an important tool for dealing with uncertain problems and information. Mutual information (MI) measures dependencies between random variables and is therefore a suitable feature selection technique for evaluating the relationship between variables in a complex classification tasks. Using Bayesian network as a statistical model, this study leverages mutual information to build a credit scoring model called BNMI. The learned Bayesian network structure is adaptively adjusted according to mutual information. Empirical study compared the results of BNMI with three existing baseline models. The results show that the proposed model outperforms the baseline models in terms of receiver operating characteristic (ROC), indicating promising application of our BNMI in the credit scoring area.