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Fair Pricing in Long-Term Insurance: A Unified Framework

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  • Hong Beng Lim
  • Mengyi Xu
  • Kenneth Q. Zhou

Abstract

Extant literature on fair pricing methods for actuarial contexts has primarily focused on the regression setting. While such approaches are well-suited to short-term products, it is unclear how they generalize to long-term products, whose pricing essentially relies on estimating transition rates in multi-state models. To address this gap, we propose a unified framework that recasts the estimation of any given multi-state transition model as a set of Poisson regression problems. This reformulation enables the direct application of existing fair pricing methods, which together constitute our proposed methodology. As an illustration, we apply the framework to a fair pricing exercise for a stylized long-term care insurance product using data from the University of Michigan Health and Retirement Study (HRS), focusing on a post-processing approach. We further explain how the framework readily accommodates pre-processing and in-processing fairness methods.

Suggested Citation

  • Hong Beng Lim & Mengyi Xu & Kenneth Q. Zhou, 2026. "Fair Pricing in Long-Term Insurance: A Unified Framework," Papers 2602.04791, arXiv.org.
    • Handle: RePEc:arx:papers:2602.04791
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    References listed on IDEAS

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    1. Renshaw, A. E. & Haberman, S., 1995. "On the graduations associated with a multiple state model for permanent health insurance," Insurance: Mathematics and Economics, Elsevier, vol. 17(1), pages 1-17, August.
    2. Lindholm, M. & Richman, R. & Tsanakas, A. & Wüthrich, M.V., 2022. "Discrimination-Free Insurance Pricing," ASTIN Bulletin, Cambridge University Press, vol. 52(1), pages 55-89, January.
    3. Olivier Côté & Marie‐Pier Côté & Arthur Charpentier, 2025. "A fair price to pay: Exploiting causal graphs for fairness in insurance," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 92(1), pages 33-75, March.
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    5. Qiqi Wang & Katja Hanewald & Xiaojun Wang, 2022. "Multistate health transition modeling using neural networks," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 89(2), pages 475-504, June.
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