Learning Bayesian network classifiers for credit scoring using Markov chain Monte Carlo search

In this paper, we evaluate the power and usefulness of Bayesian network classifiers (probabilistic networks) for credit scoring. Various types of Bayesian network classifiers are evaluated and contrasted including unrestricted Bayesian network classifiers learning using Markov Chain Monte Carlo (MCMC) search. Experiments were carried out on three real life credit scoring data sets. It is shown that MCMC Bayesian network classifiers have a very good performance and by using the Markov blanket concept, a natural form of feature selection is obtained, which results in parsimonious and powerful models for financial credit scoring.