Auto claim fraud detection using Bayesian learning neural networks
Viaene, S Dedene, G Derrig, RA
Viaene, S
Dedene, G
Derrig, RA
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Publication Type
Journal article with impact factor
Editor
Supervisor
Publication Year
2005-10
Journal
Expert Systems with Applications
Book
Publication Volume
29
Publication Issue
3
Publication Begin page
653
Publication End page
666
Publication Number of pages
Collections
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.
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Keywords
46 Information and Computing Sciences, 4611 Machine Learning