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Traditional versus AI-Based Fraud Detection: Cost Efficiency in the Field of Automobile Insurance

Author

Listed:
  • Botond Benedek

    (Babes-Bolyai University, Cluj-Napoca)

  • Balint Zsolt Nagy

    (Babes-Bolyai University, Cluj-Napoca)

Abstract

Business practice and various industry reports all show that automobile insurance fraud is very common, which is why effective fraud detection is so important. In our study, we investigate whether today's widespread AI-based fraud detection methods are more effective from a financial (cost-effectiveness) point of view than methods based on traditional statistical-econometric tools. Based on our results, we came to the unexpected conclusion that the current AI-based automobile insurance fraud detection methods tested on a real database found in the literature are less cost-effective than traditional statistical-econometric methods.

Suggested Citation

  • 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.
    • Handle: RePEc:mnb:finrev:v:22:y:2023:i:2:p:77-98
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    References listed on IDEAS

    as
    1. El Bachir Belhadji & George Dionne & Faouzi Tarkhani, 2000. "A Model for the Detection of Insurance Fraud*," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 25(4), pages 517-538, October.
    2. Miklós Virag & Tamás Nyitrai, 2013. "Application of support vector machines on the basis of the first Hungarian bankruptcy model," Society and Economy, Akadémiai Kiadó, Hungary, vol. 35(2), pages 227-248, August.
    3. Balazs J. Csillag & Marcell P. Granat & Gabor Neszveda, 2022. "Media Attention to Environmental Issues and ESG Investing," Financial and Economic Review, Magyar Nemzeti Bank (Central Bank of Hungary), vol. 21(4), pages 129-149.
    4. 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.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    automobile insurance; insurance fraud; fraud detection; cost-sensitive decision-making; data mining;
    All these keywords.

    JEL classification:

    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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