Strategies for detecting fraudulent claims in the automobile insurance industry
AbstractSome property and casualty insurers use automated detection systems to help to decide whether or not to investigate claims suspected of fraud. Claim screening systems benefit from the coded experience of previously investigated claims. The embedded detection models typically consist of scoring devices relating fraud indicators to some measure of suspicion of fraud. In practice these scoring models often focus on minimizing the error rate rather than on the cost of (mis)classification. We show that focusing on cost is a profitable approach. We analyse the effects of taking into account information on damages and audit costs early on in the screening process. We discuss several scenarios using real-life data. The findings suggest that with claim amount information available at screening time detection rules can be accommodated to increase expected profits. Our results show the value of cost-sensitive claim fraud screening and provide guidance on how to render this strategy operational. (c) 2005 Elsevier B.V. All rights reserved.
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Bibliographic InfoPaper provided by Katholieke Universiteit Leuven in its series Open Access publications from Katholieke Universiteit Leuven with number urn:hdl:123456789/98999.
Date of creation: Jan 2007
Date of revision:
Publication status: Published in European journal of operational research (2007-01) v.176, p.565-583
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Web page: http://www.kuleuven.be
decision support systems; insurance fraud; vehicle damage claims; classification; cost asymmetries; classification; market;
Other versions of this item:
- Viaene, Stijn & Ayuso, Mercedes & Guillen, Montserrat & Van Gheel, Dirk & Dedene, Guido, 2007. "Strategies for detecting fraudulent claims in the automobile insurance industry," European Journal of Operational Research, Elsevier, vol. 176(1), pages 565-583, January.
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