Abstract
This short note supplements the paper by Glschössl et al. (Eur Actuar J 1:23–41, 2011) with an efficient method allowing actuaries to include continuous covariates in their life tables, such as the sum insured for instance. Compared to the classical approach based on grouped data adopted in the majority of actuarial mortality studies, individual observations recorded at the policy level are included in the Poisson regression model. The proposed procedure avoids any preliminary, subjective banding of the range of continuous covariates that may bias the resulting life tables. The approach is illustrated on a numerical example demonstrating its advantages when individual mortality data are available.
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Acknowledgements
The authors wish to express their gratitude to the anonymous referee and the editor for useful suggestions that greatly helped to improve a previous version of this text. The present work originated from interesting discussions with Jérémy Goris, Marine Habart and Tom Popa from the Retirement and Pension Funds team of the AXA Group, based in Paris, France. The authors wish to thank the AXA team for these fruitful interactions that have inspired the present note to a large extent.
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Denuit, M., Legrand, C. Risk classification in life and health insurance: extension to continuous covariates. Eur. Actuar. J. 8, 245–255 (2018). https://doi.org/10.1007/s13385-018-0171-9
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DOI: https://doi.org/10.1007/s13385-018-0171-9