Selection Bias and Auditing Policies for Insurance Claims
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DOI: 10.1111/j.1539-6975.2007.00219.x
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Other versions of this item:
- Jean Pinquet & Guillén Montserrat & Mercedes Ayuso, 2007. "Selection bias and auditing policies for insurance claims," Post-Print hal-00397272, HAL.
- Mercedes Ayuso & Montserrat Guillén & Jean Pinquet, 2007. "Selection bias and auditing policies for insurance claims," Post-Print hal-00243035, HAL.
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Citations
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Cited by:
- Dionne, Georges, 2012.
"The empirical measure of information problems with emphasis on insurance fraud and dynamic data,"
Working Papers
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- Georges Dionne, 2012. "The Empirical Measure of Information Problems with Emphasis on Insurance Fraud and Dynamic Data," Cahiers de recherche 1233, CIRPEE.
- Benedek Botond & László Ede, 2019. "Identifying Key Fraud Indicators in the Automobile Insurance Industry Using SQL Server Analysis Services," Studia Universitatis Babeș-Bolyai Oeconomica, Sciendo, vol. 64(2), pages 53-71, August.
- Jing Ai & Patrick L. Brockett & Linda L. Golden & Montserrat Guillén, 2013. "A Robust Unsupervised Method for Fraud Rate Estimation," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 80(1), pages 121-143, March.
- Ina Garnefeld & Andreas Eggert & Markus Husemann-Kopetzky & Eva Böhm, 2019. "Exploring the link between payment schemes and customer fraud: a mental accounting perspective," Journal of the Academy of Marketing Science, Springer, vol. 47(4), pages 595-616, July.
- 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.
- Yankol-Schalck, Meryem, 2022. "The value of cross-data set analysis for automobile insurance fraud detection," Research in International Business and Finance, Elsevier, vol. 63(C).
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