The present work verifies the applicability of Bayesian Classifiers over Databases from an energy distribution company. The purpose is finding patterns or profiles into determined energy consumption groups and to estimate the number of insolvent clients. The predictive computational system identifies patterns related to each client historic and projects probable behaviors. Insolvency predictions from a Bayesian Network Augmented Naïve-Bayes (BAN) Classifier are compared to results obtained by a Tree Augmented Naïve-Bayes (TAN) Classifier and a Naïve-Bayes (NB) Classifier, taking into account historical insolvency records. Validity is verified by comparing prediction errors. Conclusions suggest an adequate approach that offers arguments for elaborating effective commercial policies for reducing insolvency.