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Insurance analytics with clustering techniques

Author

Listed:
  • Jamotton, Charlotte

    (Université catholique de Louvain, LIDAM/ISBA, Belgium)

  • Hainaut, Donatien

    (Université catholique de Louvain, LIDAM/ISBA, Belgium)

  • Hames, Thomas

    (Detralytics)

Abstract

The k-means algorithm and its variants are popular clustering techniques. Their purpose is to uncover group structures in a dataset. In actuarial applications, these partitioning methods detect clusters of policies with similar features and allow one to draw up a map of dominant risks. The main challenge lies in de􏰂ning a distance between two observations exclusively characterised by categorical variables. This research paper starts with a review of the k-means algorithm and develops an extension based on Burt's framework to manage categorical rating factors. We then focus on a mini-batch version that keeps computation time under control when analysing a large-scale dataset. We next broaden the scope of application of the fuzzy k-means to fully categorised datasets. Lastly, we conclude with a thorough introduction to spectral clustering and work around the dimensionality issue by reducing the size of the initial dataset with k-means.

Suggested Citation

  • Jamotton, Charlotte & Hainaut, Donatien & Hames, Thomas, 2023. "Insurance analytics with clustering techniques," LIDAM Discussion Papers ISBA 2023002, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    • Handle: RePEc:aiz:louvad:2023002
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    References listed on IDEAS

    as
    1. Felix Mbuga & Cristina Tortora, 2021. "Spectral Clustering of Mixed-Type Data," Stats, MDPI, vol. 5(1), pages 1-11, December.
    2. 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).
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    Cited by:

    1. Jamotton, Charlotte & Hainaut, Donatien, 2024. "Latent Dirichlet Allocation for structured insurance data," LIDAM Discussion Papers ISBA 2024008, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).

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    More about this item

    Keywords

    Clustering analysis ; unsupervised learning ; k-means ; spectral clustering;
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