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Latent Dirichlet Allocation for structured insurance data

Author

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  • Jamotton, Charlotte

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

  • Hainaut, Donatien

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

Abstract

This article explores the application of Latent Dirichlet Allocation (LDA) to structured tabular insurance data. LDA is a probabilistic topic modelling approach initially developed in Natural Language Processing (NLP) to uncover the underlying structure of (unstructured) textual data. It was designed to represent textual documents as mixture of latent (hidden) topics, and topics as mixtures of words. This study introduces the LDA’s document-topic distribution as a soft clustering tool for unsupervised learningtasks in the actuarial field. By defining each topic as a risk profile, and by treating insurance policies as documents and the modalities of categorical covariates as words, we show how LDA can be extended beyond textual data and can offer a framework to uncover underlying structures within insurance portfolios. Our experimental results and analysis highlight how the modelling of policies based on topic cluster membership, and the identification of dominant modalities within each risk profile, can give insights into the prominent risk factors contributing to higher or lower claim frequencies.

Suggested Citation

  • 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).
    • Handle: RePEc:aiz:louvad:2024008
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    References listed on IDEAS

    as
    1. Richman, Ronald, 2021. "AI in actuarial science – a review of recent advances – part 2," Annals of Actuarial Science, Cambridge University Press, vol. 15(2), pages 230-258, July.
    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.
    3. Ng, Kai Wang & Tang, Man-Lai & Tan, Ming & Tian, Guo-Liang, 2008. "Grouped Dirichlet distribution: A new tool for incomplete categorical data analysis," Journal of Multivariate Analysis, Elsevier, vol. 99(3), pages 490-509, March.
    4. 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).
    5. Richman, Ronald, 2021. "AI in actuarial science – a review of recent advances – part 1," Annals of Actuarial Science, Cambridge University Press, vol. 15(2), pages 207-229, July.
    6. Campbell, Malcolm, 1986. "An Integrated System for Estimating the Risk Premium of Individual Car Models in Motor Insurance," ASTIN Bulletin, Cambridge University Press, vol. 16(2), pages 165-183, November.
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