Auto claim fraud detection using Bayesian learning neural networks

This article explores the explicative capabilities of neural network classifiers with automatic relevance determination weight regularization, and reports the findings from applying these networks for personal injury protection automobile insurance claim fraud detection. The automatic relevance determination objective function scheme provides us with a way to determine which inputs are most informative to the trained neural network model. An implementation of MacKay's, (1992a,b) evidence framework approach to Bayesian learning is proposed as a practical way of training such networks. The empirical evaluation is based on a data set of closed claims from accidents that occurred in Massachusetts, USA during 1993.