Case-based reasoning combined with neural networks for credit risk analysis

Artificial neural networks (ANN) and related algorithms have been successfully applied to many pattern classification applications. In credit risk analysis, simple networks such as the multilayer perceptron (MLP) working alone and as ensembles have shown attractive performances when compared to more traditional statistical techniques and other market approaches. Despite that, new individual or combined models continue to be investigated to improve classifier performance in complex data problems involving large volumes of data, many correlated variables, and, noisy, incomplete, and inconsistent data. A particular attention for improving classifier accuracy should be given to the responses provided by the classifier in the frontiers that separate one class from the other. Once defining a border between two or more classes, it is expected that some of the cases very close to the border zones are incorrectly classified, due to the proximity of their pattern characteristics in that area. The purpose of this work is to investigate how a model based on case based reasoning (CBR) can be constructed to carry out a re-classification process for patterns around class frontiers. It is shown how the model performs in a large-scale credit risk assessment problem with data gathered from the PAKDD 2009 credit risk analysis competition. The performance of the CBR model combined with a MLP network is compared to that achieved by the MLP network alone with a 99% confidence level and it is shown that the model studied is promising when dealing with boundary data patterns.