Fair Pricing in Long-Term Insurance: A Unified Framework

TL;DR

This paper introduces a unified framework for fair pricing in long-term insurance by reformulating multi-state models as Poisson regressions, enabling the application of existing fairness methods to complex actuarial models.

Contribution

It extends fair pricing techniques from short-term to long-term insurance by providing a general, adaptable framework for multi-state models.

Findings

Framework successfully applied to long-term care insurance data

Enables integration of pre- and in-processing fairness methods

Facilitates fair pricing in complex actuarial models

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.