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Selection bias and auditing policies for insurance claims

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Author Info

  • Mercedes Ayuso

    (Universitat de Barcelona)

  • Montserrat Guillén

    (Universitat de Barcelona)

  • Jean Pinquet

    (Department of Economics, Ecole Polytechnique - CNRS : UMR7176 - Polytechnique - X)

Abstract

Les biais de sélection sont créés par des disparités entre le domaine d'estimation d'un modèle statistique et son domaine d'application. C'est le cas pour les modèles évaluant le risque de fraude, qui sont estimés sur les seuls sinistres audités mais appliqués sur tous les sinistres entrants. Or les sinistres audités sont une minorité, étant choisis suite à une sélection sévère effectuée par des experts. Ce papier présente une approche statistique qui contrebalance le biais de sélection sans recourir à une stratégie d'audit aléatoire. On estime un modéle à deux équations sur l'audit et la fraude (un modèle probit bivarié avec censure), sur une une partie d'une base de sinistres où les experts sont laissés libres de leur décision d'auditer. On corrige ainsi la surestimation attendue du risque de fraude en cas d'estimation d'une seule équation. Les résultats sont proches de ceux obtenus par audit aléatoire, au prix d'une instabilité des résultats par rapport à l'ensemble des composantes de régression. On compare ensuite des politiques d'audit à partir des différentes approches.

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Bibliographic Info

Paper provided by HAL in its series Post-Print with number hal-00243035.

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Date of creation: 2007
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Publication status: Published, The Journal of Risk and Insurance, 2007, 74, 2, 425-440
Handle: RePEc:hal:journl:hal-00243035

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References

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  1. Heckman, James J, 1979. "Sample Selection Bias as a Specification Error," Econometrica, Econometric Society, Econometric Society, vol. 47(1), pages 153-61, January.
  2. Dionne, Georges & Gagne, Robert & Vanasse, Charles, 1998. "Inferring technological parameters from incomplete panel data," Journal of Econometrics, Elsevier, Elsevier, vol. 87(2), pages 303-327, September.
  3. Dionne, G. & St-Michel, P. & Gibbens, A., 1993. "An Economic Analysis of Insurance Fraud," Cahiers de recherche, Centre interuniversitaire de recherche en économie quantitative, CIREQ 93010, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
  4. El Bachir Belhadji & George Dionne & Faouzi Tarkhani, 2000. "A Model for the Detection of Insurance Fraud*," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan, vol. 25(4), pages 517-538, October.
  5. G. Dionne & F. Giuliano & P. Picard, 2002. "Optimal auditing for insurance fraud," THEMA Working Papers 2002-32, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
  6. Steven B. Caudill & Mercedes Ayuso & Montserrat Guillén, 2005. "Fraud Detection Using a Multinomial Logit Model With Missing Information," Journal of Risk & Insurance, The American Risk and Insurance Association, The American Risk and Insurance Association, vol. 72(4), pages 539-550.
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Cited by:
  1. 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.
  2. Georges Dionne, 2012. "The Empirical Measure of Information Problems with Emphasis on Insurance Fraud and Dynamic Data," Cahiers de recherche, CIRPEE 1233, CIRPEE.

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