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Fraud Detection Using a Multinomial Logit Model With Missing Information

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  • Steven B. Caudill
  • Mercedes Ayuso
  • Montserrat Guillén

Abstract

Recently, Artís, Ayuso, and Guillén (2002, Journal of Risk and Insurance 69: 325–340; henceforth AAG) estimate a logit model using claims data. Some of the claims are categorized as “honest” and other claims are known to be fraudulent. Using the approach of Hausman, Abrevaya, and Scott‐Morton (1998 Journal of Econometrics 87: 239‐269), AAG estimate a modified logit model allowing for the possibility that some claims classified as honest might actually be fraudulent. Applying this model to data on Spanish automobile insurance claims, AGG find that 5 percent of the fraudulent claims go undetected. The purpose of this article is to estimate the model of AAG using a logit model with missing information. A constrained version of this model is used to reexamine the Spanish insurance claim data. The results indicate how to identify misclassified claims. We also show how misclassified claims can be identified using the AAG approach. We show that both approaches can be used to probabilistically identify misclassified claims.

Suggested Citation

  • 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, December.
    • Handle: RePEc:bla:jrinsu:v:72:y:2005:i:4:p:539-550
    DOI: 10.1111/j.1539-6975.2005.00137.x
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    References listed on IDEAS

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    1. Artis, Manuel & Ayuso, Mercedes & Guillen, Montserrat, 1999. "Modelling different types of automobile insurance fraud behaviour in the Spanish market," Insurance: Mathematics and Economics, Elsevier, vol. 24(1-2), pages 67-81, March.
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    Cited by:

    1. Yeh Jason Jia-Hsing, 2009. "Missing (Completely?) At Random: Lessons from Insurance Studies," Asia-Pacific Journal of Risk and Insurance, De Gruyter, vol. 3(2), pages 1-13, April.
    2. Dionne, Georges, 2012. "The empirical measure of information problems with emphasis on insurance fraud and dynamic data," Working Papers 12-10, HEC Montreal, Canada Research Chair in Risk Management.
    3. Urbina, Jilber & Guillén, Montserrat, 2013. "An application of capital allocation principles to operational risk," MPRA Paper 75726, University Library of Munich, Germany, revised Dec 2013.
    4. Paulina Oliva, 2015. "Environmental Regulations and Corruption: Automobile Emissions in Mexico City," Journal of Political Economy, University of Chicago Press, vol. 123(3), pages 686-724.
    5. 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.
    6. Pulina, Manuela & Paba, Antonello, 2010. "A discrete choice approach to model credit card fraud," MPRA Paper 20019, University Library of Munich, Germany.
    7. 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, June.
    8. Ming-Jyh Wang & Chieh-Hua Wen & Lawrence W Lan, 2010. "Modelling Different Types of Bundled Automobile Insurance Choice Behaviour: The Case of Taiwan*," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 35(2), pages 290-308, April.
    9. Hunsoo Kim & W. Jean Kwon, 2006. "A Multi‐Line Insurance Fraud Recognition System: A Government‐Led Approach in Korea," Risk Management and Insurance Review, American Risk and Insurance Association, vol. 9(2), pages 131-147, September.
    10. Michele Tumminello & Andrea Consiglio & Pietro Vassallo & Riccardo Cesari & Fabio Farabullini, 2023. "Insurance fraud detection: A statistically validated network approach," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 90(2), pages 381-419, June.
    11. Andersson, Jonas & Olden, Andreas & Rusina, Aija, 2020. "Fraud detection by a multinomial model: Separating honesty from unobserved fraud," Discussion Papers 2020/15, Norwegian School of Economics, Department of Business and Management Science.
    12. Steven B. Caudill & Peter A. Groothuis, 2005. "Modeling Hidden Alternatives in Random Utility Models: An Application to "Don’t Know" Responses in Contingent Valuation," Land Economics, University of Wisconsin Press, vol. 81(3).
    13. 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.
    14. Chu-Shiu Li & Chwen-Chi Liu & Sheng-Chang Peng, 2013. "Expiration Dates in Automobile Insurance Contracts: The Curious Case of Last Policy Month Claims in Taiwan," The Geneva Risk and Insurance Review, Palgrave Macmillan;International Association for the Study of Insurance Economics (The Geneva Association), vol. 38(1), pages 23-47, March.
    15. 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).
    16. Denisa BANULESCU-RADU & Meryem YANKOL-SCHALCK, 2021. "Fraud detection in the era of Machine Learning: a household insurance case," LEO Working Papers / DR LEO 2904, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.

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