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Insurance Analytics with Clustering Techniques

Author

Listed:
  • Charlotte Jamotton

    (Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA), Université Catholique de Louvain (UCLouvain), 1348 Louvain-la-Neuve, Belgium)

  • Donatien Hainaut

    (Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA), Université Catholique de Louvain (UCLouvain), 1348 Louvain-la-Neuve, Belgium)

  • Thomas Hames

    (Detralytics, Rue Belliard 2-B, 1040 Brussels, Belgium)

Abstract
The K-means algorithm and its variants are well-known clustering techniques. In actuarial applications, these partitioning methods can identify clusters of policies with similar attributes. The resulting partitions provide an actuarial framework for creating maps of dominant risks and unsupervised pricing grids. This research article aims to adapt well-established clustering methods to complex insurance datasets containing both categorical and numerical variables. To achieve this, we propose a novel approach based on Burt distance. We begin by reviewing the K-means algorithm to establish the foundation for our Burt distance-based framework. Next, we extend the scope of application of the mini-batch and fuzzy K-means variants to heterogeneous insurance data. Additionally, we adapt spectral clustering, a technique based on graph theory that accommodates non-convex cluster shapes. To mitigate the computational complexity associated with spectral clustering’s O ( n 3 ) runtime, we introduce a data reduction method for large-scale datasets using our Burt distance-based approach.

Suggested Citation

  • Charlotte Jamotton & Donatien Hainaut & Thomas Hames, 2024. "Insurance Analytics with Clustering Techniques," Risks, MDPI, vol. 12(9), pages 1-28, September.
    • Handle: RePEc:gam:jrisks:v:12:y:2024:i:9:p:141-:d:1472255
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    References listed on IDEAS

    as
    1. Hainaut, Donatien, 2019. "A self-organizing predictive map for non-life insurance," LIDAM Reprints ISBA 2019026, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    2. Guojun Gan & Emiliano A. Valdez, 2020. "Data Clustering with Actuarial Applications," North American Actuarial Journal, Taylor & Francis Journals, vol. 24(2), pages 168-186, April.
    Full references (including those not matched with items on IDEAS)

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