Bayesian regularized artificial neural networks for the estimation of the probability of default

Abstract

Artificial neural networks (ANNs) have been extensively used for classification problems in many areas such as gene, text and image recognition. Although ANNs are popular also to estimate the probability of default in credit risk, they have drawbacks; a major one is their tendency to overfit the data. Here we propose an improved Bayesian regularization approach to train ANNs and compare it to the classical regularization that relies on the back-propagation algorithm for training feed-forward networks. We investigate different network architectures and test the classification accuracy on three data sets. Profitability, leverage and liquidity emerge as important financial default driver categories.