Bayesian Network Modeling: A Case Study of Credit Scoring Analysis of Consumer Loans Default Payment

Abstract

This paper deals with the issue of predicting customers’ default payment. The Bayesian network credit model is applied for the prediction and classification of personal loans with regard to credit worthiness. Referring to credit experts and using K2 algorithm for learning structure, we set up the dependency conditional relations between variables that explain default payments. Then, the parametric learning is adopted to detect conditional probabilities of customers’ default payment. The parameters are estimated on the basis of real personal loan data obtained from a Tunisian commercial bank. The Bayesian network analysis has revealed that customers’ age, gender, type of credit, professional status, and monthly repayment burden and credit duration have an important predictive power for the detection of customers’ default payment. Therefore, our findings allow providing an effective decision support system for banks in order to detect and reduce the rate of bad borrowers through the use of a Bayesian Network model.