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Risk classification in life insurance: methodology and case study

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Abstract

In this paper, we describe how Poisson regression analysis can be efficiently used to perform graduation of mortality rates in presence of exogenous information supporting an efficient underwriting process in life insurance business. After having justified the relevance of a Poisson likelihood for mortality data, we explain how categorical and continuous covariates can be included in the model. A case study based on a German insurance portfolio is proposed to illustrate the usefulness of the approach described in this paper.

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Acknowledgments

The authors would like to thank an anonymous reviewer whose suggestions improved the original manuscript. The data analysis in this paper was performed with R , statistical software which is released under the GNU General Public License (GPL). For more information on R , the interested reader is referred to R Development Core Team [14]. Beyond the R code we conceived ourselves, we benefitted in particular from the locfit package, described in Loader [12].

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Authors and Affiliations

  1. Munich Reinsurance Company, Divisional Unit: Life, Munich, Germany

    Susanne Gschlössl & Pascal Schoenmaekers

  2. Institut de statistique, biostatistique et sciences actuarielles (ISBA), Université Catholique de Louvain, Louvain-la-Neuve, Belgium

    Michel Denuit

Authors
  1. Susanne Gschlössl
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  2. Pascal Schoenmaekers
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  3. Michel Denuit
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Corresponding author

Correspondence to Michel Denuit.

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The views and opinions of authors expressed herein do not necessarily state or reflect those of the mentioned institutions.

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Gschlössl, S., Schoenmaekers, P. & Denuit, M. Risk classification in life insurance: methodology and case study. Eur. Actuar. J. 1, 23–41 (2011). https://doi.org/10.1007/s13385-011-0028-y

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  • DOI: https://doi.org/10.1007/s13385-011-0028-y

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