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Grouping of Contracts in Insurance using Neural Networks

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  • Mark Kiermayer
  • Christian Wei{ss}

Abstract

Despite the high importance of grouping in practice, there exists little research on the respective topic. The present work presents a complete framework for grouping and a novel method to optimize model points. Model points are used to substitute clusters of contracts in an insurance portfolio and thus yield a smaller, computationally less burdensome portfolio. This grouped portfolio is controlled to have similar characteristics as the original portfolio. We provide numerical results for term life insurance and defined contribution plans, which indicate the superiority of our approach compared to K-means clustering, a common baseline algorithm for grouping. Lastly, we show that the presented concept can optimize a fixed number of model points for the entire portfolio simultaneously. This eliminates the need for any pre-clustering of the portfolio, e.g. by K-means clustering, and therefore presents our method as an entirely new and independent methodology.

Suggested Citation

  • Mark Kiermayer & Christian Wei{ss}, 2019. "Grouping of Contracts in Insurance using Neural Networks," Papers 1912.09964, arXiv.org.
    • Handle: RePEc:arx:papers:1912.09964
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    References listed on IDEAS

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    1. Denuit, Michel & Trufin, Julien, 2015. "Model points and Tail-VaR in life insurance," Insurance: Mathematics and Economics, Elsevier, vol. 64(C), pages 268-272.
    2. Seyed Amir Hejazi & Kenneth R. Jackson, 2016. "Efficient Valuation of SCR via a Neural Network Approach," Papers 1610.01946, arXiv.org.
    3. Anne-Sophie Krah & Zoran Nikolić & Ralf Korn, 2018. "A Least-Squares Monte Carlo Framework in Proxy Modeling of Life Insurance Companies," Risks, MDPI, vol. 6(2), pages 1-26, June.
    4. Denuit, Michel & Trufin, Julien, 2015. "Model points and Tail-VaR in life insurance," LIDAM Reprints ISBA 2015020, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    5. Frostig, Esther, 2001. "A comparison between homogeneous and heterogeneous portfolios," Insurance: Mathematics and Economics, Elsevier, vol. 29(1), pages 59-71, August.
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    Cited by:

    1. Anne-Sophie Krah & Zoran Nikolić & Ralf Korn, 2020. "Least-Squares Monte Carlo for Proxy Modeling in Life Insurance: Neural Networks," Risks, MDPI, vol. 8(4), pages 1-21, November.

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