Abstract:

The current paper deals with the issue of predicting customers' default payment. One of the most effective classification methods in credit scoring applications is the Bayesian Network method. The Bayesian network credit model is applied for the prediction and classification of personal loan customers with regard to credit worthiness. Relying on information taken from credit experts and using K2 algorithm for learning structure, we constructed the dependency conditional relations between variables explaining 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 revealed that customers' age, gender, type of credit, professional status, the monthly repayment burden, and credit duration have an important predictive power for the detection of customers' default payment. Therefore, our findings serve to provide an effective decision support system for banks to detect and alleviate the rate of bad borrowers through the use of a Bayesian Network model.