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Network analytics for insurance fraud detection: a critical case study

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Abstract

There has been an increasing interest in fraud detection methods, driven by new regulations and by the financial losses linked to fraud. One of the state-of-the-art methods to fight fraud is network analytics. Network analytics leverages the interactions between different entities to detect complex patterns that are indicative of fraud. However, network analytics has only recently been applied to fraud detection in the actuarial literature. Although it shows much potential, many network methods are not yet applied. This paper extends the literature in two main ways. First, we review and apply multiple methods in the context of insurance fraud and assess their predictive power against each other. Second, we analyse the added value of network features over intrinsic features to detect fraud. We conclude that (1) complex methods do not necessarily outperform basic network features, and that (2) network analytics helps to detect different fraud patterns, compared to models trained on claim-specific features alone.

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Data Availability

The healthcare provider data is available on kaggle (https://www.kaggle.com/datasets/rohitrox/healthcare-provider-fraud-detection-analysis). The motor insurance data set is proprietary.

Notes

  1. https://www.kaggle.com/datasets/rohitrox/healthcare-provider-fraud-detection-analysis.

  2. https://github.com/B-Deprez/NetworkFraud_BiRank_M2V_SAGE.

  3. Among an insurer’s (independent) companies over state lines.

  4. Among subsidiaries of an insurance company.

  5. Among different agents involved, i.e., hospitals, patients and pharmacies.

  6. Since claim and fraud data are highly sensitive, we only give a rough approximation of the numbers, which can either be rounded up or down. The total number is the sum of these semi-random numbers.

  7. https://www.kaggle.com/datasets/rohitrox/healthcare-provider-fraud-detection-analysis.

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Funding

This work was supported by the Research Foundation—Flanders (FWO research project 1SHEN24N).

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Authors and Affiliations

  1. Faculty of Economics and Business, KU Leuven, Naamsestraat 69, 3000, Leuven, Belgium

    Bruno Deprez, Félix Vandervorst, Wouter Verbeke & Bart Baesens

  2. Department of Mathematics, University of Antwerp, Middelheimlaan 1, 2020, Antwerp, Belgium

    Bruno Deprez, Félix Vandervorst & Tim Verdonck

  3. Department of Mathematics, KU Leuven, Celestijnenlaan 200B, 3001, Leuven, Belgium

    Tim Verdonck

  4. Data Office, Allianz Benelux, Koning Albert II Laan 32, 1000, Brussels, Belgium

    Félix Vandervorst

  5. Department of Decision Analytics and Risk, University of Southampton, University Road, Southampton, SO17 1BJ, UK

    Bart Baesens

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  1. Bruno Deprez
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  2. Félix Vandervorst
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  3. Wouter Verbeke
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  4. Tim Verdonck
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  5. Bart Baesens
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Correspondence to Bruno Deprez.

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Deprez, B., Vandervorst, F., Verbeke, W. et al. Network analytics for insurance fraud detection: a critical case study. Eur. Actuar. J. (2024). https://doi.org/10.1007/s13385-024-00384-6

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