🤖 AI Summary
This study addresses the challenge that existing fair pricing methods, primarily designed for short-term insurance, are ill-suited for long-term insurance products governed by multi-state transition models. To bridge this gap, the authors reformulate the estimation of multi-state transition rates as a series of Poisson regression problems, thereby establishing the first unified framework for fair pricing in long-term insurance. This framework seamlessly integrates with mainstream fair machine learning techniques and accommodates preprocessing, in-processing, and post-processing fairness strategies. Using data from the University of Michigan’s Health and Retirement Study, the approach is validated in the context of long-term care insurance pricing, demonstrating both effectiveness and flexibility while filling a critical methodological void between multi-state modeling and fair machine learning.
📝 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.