The granting of loans by a financial institution (bank or home loan business) is one of the important decision problems that require delicate care. It can be performed using a variety of different processing algorithms and tools. Neural networks are considered one of the most promising approaches. In this study, optimal parameters and the comparative efficiency and accuracy of three models: Multi Layer Perceptron, Ensemble Averaging and Boosting by Filtering have been investigated in the light of credit loan application classification. The goal was to find the best tool among the three neural network models for this kind of decision context. The experimental results indicate that Committee Machine models were superior to a single Multi Layer Perceptron model, and that Boosting by Filtering outperformed Ensemble Averaging.