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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.
    2. 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.
    3. Pierre Picard, 2012. "Economic Analysis of Insurance Fraud," Working Papers hal-00725561, HAL.
    4. Cramer, J. S. & Ridder, G., 1991. "Pooling states in the multinomial logit model," Journal of Econometrics, Elsevier, vol. 47(2-3), pages 267-272, February.
    5. 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.
    6. Dionne, G., 1980. "The Effects of Insurance on the Possibilities of Fraud," Cahiers de recherche 8103, Universite de Montreal, Departement de sciences economiques.
    7. Ernst R. Berndt & Bronwyn H. Hall & Robert E. Hall & Jerry A. Hausman, 1974. "Estimation and Inference in Nonlinear Structural Models," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 3, number 4, pages 653-665, National Bureau of Economic Research, Inc.
    8. 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.
    9. Hausman, J. A. & Abrevaya, Jason & Scott-Morton, F. M., 1998. "Misclassification of the dependent variable in a discrete-response setting," Journal of Econometrics, Elsevier, vol. 87(2), pages 239-269, September.
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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. 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).
    3. Pulina, Manuela & Paba, Antonello, 2010. "A discrete choice approach to model credit card fraud," MPRA Paper 20019, University Library of Munich, Germany.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    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. Urbina, Jilber & Guillén, Montserrat, 2013. "An application of capital allocation principles to operational risk," Working Papers 2072/222201, Universitat Rovira i Virgili, Department of Economics.
    10. 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.
    11. 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).
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    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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