In this paper, we will evaluate the power and usefulness of Bayesian network classifiers (probabilistic networks) for credit scoring. Various types of Bayesian network classifiers will be evaluated and contrasted including unrestricted Bayesian network classifiers learned using Markov Chain Monte Carlo (MCMC) search. The experiments will be carried out on three real life credit scoring data sets. It will be 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.