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A Model for the Detection of Insurance Fraud*

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Author Info

  • El Bachir Belhadji
  • George Dionne

    ()
    (Risk Management Chair, HEC-Montreal.)

  • Faouzi Tarkhani

Abstract

The aim of this article is to develop a model to aid insurance companies in their decision-making and to ensure that they are better equipped to fight fraud. This tool is based on the systematic use of fraud indicators. We first propose a procedure to isolate the indicators which are most significant in predicting the probability that a claim may be fraudulent. We applied the procedure to data collected in the Dionne–Belhadji study (1996). The model allowed us to observe that 23 of the 54 indicators used were significant in predicting the probability of fraud. Our study also discusses the model's accuracy and detection capability. The detection rates obtained by the adjusters who participated in the study constitute the reference point of this discussion. As shown in the Caron–Dionne (1998), there is the possibility that these rates underestimate the true level of fraud. The Geneva Papers on Risk and Insurance (2000) 25, 517–538. doi:10.1111/1468-0440.00080

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Bibliographic Info

Article provided by Palgrave Macmillan in its journal The Geneva Papers on Risk and Insurance.

Volume (Year): 25 (2000)
Issue (Month): 4 (October)
Pages: 517-538

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Handle: RePEc:pal:gpprii:v:25:y:2000:i:4:p:517-538

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Cited by:
  1. Georges Dionne & Florence Giuliano & Pierre Picard, 2009. "Optimal Auditing with Scoring: Theory and Application to Insurance Fraud," Management Science, INFORMS, vol. 55(1), pages 58-70, January.
  2. Bermúdez, Ll. & Pérez, J.M. & Ayuso, M. & Gómez, E. & Vázquez, F.J., 2008. "A Bayesian dichotomous model with asymmetric link for fraud in insurance," Insurance: Mathematics and Economics, Elsevier, vol. 42(2), pages 779-786, April.
  3. Georges Dionne & Florence Giuliano & Pierre Picard, 2003. "Optimal Auditing for Insurance Fraud," Cahiers de recherche 0329, CIRPEE.
  4. 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.
  5. Yufei Jin & Roderick Rejesus & Bertis Little, 2005. "Binary choice models for rare events data: a crop insurance fraud application," Applied Economics, Taylor & Francis Journals, vol. 37(7), pages 841-848.
  6. Jean Pinquet & Mercedes Ayuso & Montserrat Guillén, 2007. "Selection Bias and Auditing Policies for Insurance Claims," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 74(2), pages 425-440.
  7. Georges Dionne, 2012. "The Empirical Measure of Information Problems with Emphasis on Insurance Fraud and Dynamic Data," Cahiers de recherche 1233, CIRPEE.
  8. Jörg Schiller, 2006. "The Impact of Insurance Fraud Detection Systems," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 73(3), pages 421-438.
  9. Schiller, Jörg, 2002. "The impact of insurance fraud detection systems," Working Papers on Risk and Insurance 8, University of Hamburg, Institute for Risk and Insurance.

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