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A Bayesian dichotomous model with asymmetric link for fraud in insurance

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  • Bermúdez, Ll.
  • Pérez, J.M.
  • Ayuso, M.
  • Gómez, E.
  • Vázquez, F.J.

Abstract

Standard binary models have been developed to describe the behavior of consumers when they are faced with two choices. The classical logit model presents the feature of the symmetric link function. However, symmetric links do not provide good fits for data where one response is much more frequent than the other (as it happens in the insurance fraud context). In this paper, we use an asymmetric or skewed logit link, proposed by Chen et al. [Chen, M., Dey, D., Shao, Q., 1999. A new skewed link model for dichotomous quantal response data. J. Amer. Statist. Assoc. 94 (448), 1172-1186], to fit a fraud database from the Spanish insurance market. Bayesian analysis of this model is developed by using data augmentation and Gibbs sampling. The results show that the use of an asymmetric link notably improves the percentage of cases that are correctly classified after the model estimation.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:insuma:v:42:y:2008:i:2:p:779-786
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    References listed on IDEAS

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    Cited by:

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    2. 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.
    3. Botond Benedek & Balint Zsolt Nagy, 2023. "Traditional versus AI-Based Fraud Detection: Cost Efficiency in the Field of Automobile Insurance," Financial and Economic Review, Magyar Nemzeti Bank (Central Bank of Hungary), vol. 22(2), pages 77-98.
    4. Bayerstadler, Andreas & van Dijk, Linda & Winter, Fabian, 2016. "Bayesian multinomial latent variable modeling for fraud and abuse detection in health insurance," Insurance: Mathematics and Economics, Elsevier, vol. 71(C), pages 244-252.
    5. Xia, Changyuan & Yang, Junjie & Yang, Zeng & Chan, Kam C., 2023. "Do directors with foreign experience increase the corporate demand for directors' and officers' liability insurance? Evidence from China," Economic Modelling, Elsevier, vol. 119(C).
    6. 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.
    7. Katja Müller & Hato Schmeiser & Joël Wagner, 2016. "The impact of auditing strategies on insurers’ profitability," Journal of Risk Finance, Emerald Group Publishing, vol. 17(1), pages 46-79, January.
    8. Daixin Wang & Zhiqiang Zhang & Yeyu Zhao & Kai Huang & Yulin Kang & Jun Zhou, 2024. "Financial Default Prediction via Motif-preserving Graph Neural Network with Curriculum Learning," Papers 2403.06482, arXiv.org.

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