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Selection bias and auditing policies for insurance claims

  • Mercedes Ayuso

    (UB - Universitat de Barcelona)

  • Montserrat Guillén

    (UB - Universitat de Barcelona)

  • Jean Pinquet

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

Selection bias results from a discrepancy between the range of estimation of a statistical model and its range of application. This is the case for fraud risk models, which are estimated on audited claims but applied on incoming claims in the design of auditing strategies. Now audited claims are a minority within the parent sample since they are chosen after a severe selection performed by claims adjusters. This paper presents a statistical approach which counteracts selection bias without using a random auditing strategy. A two equation model on audit and fraud (a bivariate probit model with censoring) is estimated on a sample of claims where the experts are left free to take the audit decision. The expected overestimation of fraud risk derived from a single equation model is corrected. Results are rather close to those obtained with a random auditing strategy, at the expense of some instability with respect to the regression components set. Then we compare auditing policies derived from the different approaches.

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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 in The Journal of Risk and Insurance, 2007, 74 (2), pp.425-440
Handle: RePEc:hal:journl:hal-00243035
Note: View the original document on HAL open archive server: https://hal.archives-ouvertes.fr/hal-00243035
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  1. 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.
  2. Georges Dionne & Florence Giuliano & Pierre Picard, 2009. "Optimal auditing for insurance fraud," Post-Print hal-00367109, HAL.
  3. Pierre Picard, 2012. "Economic Analysis of Insurance Fraud," Working Papers hal-00725561, HAL.
  4. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 31(3), pages 129-137.
  5. 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, vol. 72(4), pages 539-550.
  6. 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.
  7. Dionne, G. & Gane, R. & Vanasse, C., 1995. "Infessing Technological Parameters from Incomplete Panel Data," Cahiers de recherche 9537, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
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