Auto claim fraud detection using Bayesian learning neural networks

Authors:
S. Viaene;G. Dedene;R. A. Derrig
Affiliations:
Applied Economic Sciences, K. V. Leuvei, Naamsestraat 69, B-3000 Leuven, Belgium;Vlerick Leuven Gent Management School, Reep1, B-9000 Gent, Belgium;Automobile Insurers Bureau of Massachusetts & Insurance Fraud Bureau of Massachusetts, 101 Arch Street, Boston MA 02110, USA
Venue:
Expert Systems with Applications: An International Journal
Year:
2005

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Abstract

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. e 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.