Neural Network Classifers in Arrears Management

The literature suggests that an ensemble of classifiers outperforms a single classifier across a range of classification problems. This paper investigates the application of an ensemble of neural network classifiers to the prediction of potential defaults for a set of personal loan accounts drawn from a medium sized Australian financial institution. The imbalanced nature of the data sets necessitates the implementation of strategies to avoid under learning of the minority class and two such approaches (minority over-sampling and majority under-sampling) were adopted here. The ensemble out performed the single networks irrespective of which strategy was used. The results also compared more than favourably with those reported in the literature for a similar application