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Optimal Auditing with Scoring Theory and Application to Insurance Fraud

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Author Info

  • Georges Dionne

    (HEC Montréal - HEC MONTRÉAL)

  • Florence Giuliano

    (EconomiX - CNRS : UMR7166 - Université de Paris X - Nanterre)

  • Pierre Picard

    (CECO - Laboratoire d'econometrie de l'école polytechnique - CNRS : UMR7657 - Polytechnique - X)

Abstract

Cet article établit une liaison entre la théorie de l'audit optimal et la méthodologie du scoring dans un contexte d'asymétrie d'information. L'application retenue concerne la fraude à l'assurance, mais la même approche peut être appliquée à d'autres activités qui utilisent le scoring. Nous montrons que la stratégie et l'audit optimal consistent à transmettre les demandes d'indemnité à une cellule antifraude lorsque certains indicateurs de fraude sont observés. Les indicateurs de fraude sont classés en fonction d'une probabilité de fraude croissante. Une telle stratégie demeure optimale lorsque la politique d'enquête est budgétairement contrainte. De plus, la politique d'audit agit comme un mécanisme de dissuasion et nous expliquons pourquoi elle nécessite un engagement de l'assureur et comment elle devrait affecter les incitations financières du personnel de la cellule antifraude. Le modèle est calibré avec les données d'un grand assureur européen. Nous calculons une valeur critique d'un indice de suspicion de fraude qui fournit un seuil au delà duquel toutes les demandes d'indemnité doivent être soumises à audit et nous évaluons le gain potentiel qui pourrait être dérivé de la politique d'audit optimal. Nous montrons qu'il est possible d'améliorer ces résultats en séparant différents groupes d'assurés avec des coûts psychologiques de fraude différents. Enfin nos résultats montrent comment l'effet de dissuasion peut être pris en compte et comment il affecte la stratégie optimale d'audit.

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Bibliographic Info

Paper provided by HAL in its series Working Papers with number hal-00243026.

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Date of creation: 2005
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Handle: RePEc:hal:wpaper:hal-00243026

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  1. John Knowles & Nicola Persico & Petra Todd, . "Racial Bias in Motor Vehicle Searches: Theory and Evidence," Penn CARESS Working Papers 5940d5c4875c571776fb29700, Penn Economics Department.
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  8. Beneish, Messod D., 1997. "Detecting GAAP violation: implications for assessing earnings management among firms with extreme financial performance," Journal of Accounting and Public Policy, Elsevier, vol. 16(3), pages 271-309.
  9. Townsend, Robert M., 1988. "Information constrained insurance : The revelation principle extended," Journal of Monetary Economics, Elsevier, vol. 21(2-3), pages 411-450.
  10. Picard, Pierre, 1996. "Auditing claims in the insurance market with fraud: The credibility issue," Journal of Public Economics, Elsevier, vol. 63(1), pages 27-56, December.
  11. Peter Ove Christensen, 2002. "Accounting Policies in Agencies with Moral Hazard and Renegotiation," Journal of Accounting Research, Wiley Blackwell, vol. 40(4), pages 1071-1090, 09.
  12. M. Martin Boyer, 2004. "Overcompensation as a Partial Solution to Commitment and Renegotiation Problems: The Case of "Ex Post" Moral Hazard," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 71(4), pages 559-582.
  13. Georges Dionne & Robert Gagné, 2001. "Deductible Contracts Against Fraudulent Claims: Evidence From Automobile Insurance," The Review of Economics and Statistics, MIT Press, vol. 83(2), pages 290-301, May.
  14. Dionne, G. & St-Michel, P. & Gibbens, A., 1993. "An Economic Analysis of Insurance Fraud," Cahiers de recherche 93010, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
  15. Gale, Douglas & Hellwig, Martin, 1985. "Incentive-Compatible Debt Contracts: The One-Period Problem," Review of Economic Studies, Wiley Blackwell, vol. 52(4), pages 647-63, October.
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  17. Guillen, Montserrat & Manuel Artis, 1994. "Count Data Models For A Credit Scoring System," Working Papers 021, Risk and Insurance Archive.
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Cited by:
  1. Lang, Matthias & Wambach, Achim, 2013. "The fog of fraud – Mitigating fraud by strategic ambiguity," Games and Economic Behavior, Elsevier, vol. 81(C), pages 255-275.
  2. Samohyl, Robert, 2012. "Audits and logistic regression, deciding what really matters in service processes: a case study of a government funding agency for research grants," MPRA Paper 41557, University Library of Munich, Germany.
  3. Dominic Spengler, 2012. "Endogenising Detection in an Asymmetric Penalties Corruption Game," Discussion Papers 12/20, Department of Economics, University of York.
  4. Georges Dionne & Kili C. Wang, 2011. "Does Opportunistic Fraud in Automobile theft Insurance Fluctuate with the Business Cycle ?," Cahiers de recherche 1121, CIRPEE.
  5. Jean-Marc Bourgeon & Pierre Picard, 2012. "Fraudulent Claims and Nitpicky Insurers," Working Papers hal-00675106, HAL.
  6. Pierre Picard, 2012. "Economic Analysis of Insurance Fraud," Working Papers hal-00725561, HAL.
  7. Georges Dionne & Kili Wang, 2013. "Does insurance fraud in automobile theft insurance fluctuate with the business cycle?," Journal of Risk and Uncertainty, Springer, vol. 47(1), pages 67-92, August.
  8. Georges Dionne, 2012. "The Empirical Measure of Information Problems with Emphasis on Insurance Fraud and Dynamic Data," Cahiers de recherche 1233, CIRPEE.

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