Leveraging Top-Model Selection in Ensemble Neural Networks for Improved Credit Risk Prediction

Credit risk prediction is both a difficult and of great interest problem, due to inherently unbalanced nature of such data and continuous interest in performing the prediction with high precision. We improve previous results on credit risk prediction and present an ensemble of decision Artificial Neural Networks architecture for credit risk classification. The extensive experimental results we present show improvements of previous work on metrics including accuracy, precision, sensitivity and specificity. Unlike previous methods, our method is completely automated, eliminating the need of manual processing and selection of data features, which improves generalization and scalability. While the main focus of this work is on credit risk prediction, our analysis shows that the model we propose can be used successfully for dimensionality reduction and classification of unbalanced data, in general.