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Fraud detection in the era of Machine Learning: a household insurance case

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  • Denisa BANULESCU-RADU
  • Meryem YANKOL-SCHALCK

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  • 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.
    • Handle: RePEc:leo:wpaper:2904
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    File URL: http://data.leo-univ-orleans.fr/media/search-works/2904/dr-leo-2021-14.pdf
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    References listed on IDEAS

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    1. B Baesens & T Van Gestel & S Viaene & M Stepanova & J Suykens & J Vanthienen, 2003. "Benchmarking state-of-the-art classification algorithms for credit scoring," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(6), pages 627-635, June.
    2. Shinichi Nakagawa, 2004. "A farewell to Bonferroni: the problems of low statistical power and publication bias," Behavioral Ecology, International Society for Behavioral Ecology, vol. 15(6), pages 1044-1045, November.
    3. Viaene, Stijn & Ayuso, Mercedes & Guillen, Montserrat & Van Gheel, Dirk & Dedene, Guido, 2007. "Strategies for detecting fraudulent claims in the automobile insurance industry," European Journal of Operational Research, Elsevier, vol. 176(1), pages 565-583, January.
    4. Bart Baesens & Rudy Setiono & Christophe Mues & Jan Vanthienen, 2003. "Using Neural Network Rule Extraction and Decision Tables for Credit-Risk Evaluation," Management Science, INFORMS, vol. 49(3), pages 312-329, March.
    5. El Bachir, Belhadji & Dionne, Georges, 1997. "Development of an expert system for the automatic detection of automobile insurance fraud," Working Papers 97-6, HEC Montreal, Canada Research Chair in Risk Management.
    6. King, Gary & Zeng, Langche, 2001. "Logistic Regression in Rare Events Data," Political Analysis, Cambridge University Press, vol. 9(2), pages 137-163, January.
    7. 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.
    8. 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.
    9. Yufei Jin & Roderick Rejesus & Bertis Little, 2005. "Binary choice models for rare events data: a crop insurance fraud application," Applied Economics, Taylor & Francis Journals, vol. 37(7), pages 841-848.
    10. Joseph A. Atwood & James F. Robison-Cox & Saleem Shaik, 2006. "Estimating the Prevalence and Cost of Yield-Switching Fraud in the Federal Crop Insurance Program," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 88(2), pages 365-381.
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    Keywords

    ; Fraud detection; Household insurance; Machine learning; Logistic LASSO; XGBoost; Imbalanced data; SHAP;
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