Fair Pricing in Long-Term Insurance: A Unified Framework

Battle Count: 53

This paper is focused on actuarial insurance pricing and fairness in long-term insurance products. It has minimal direct relevance to quantitative trading, algorithmic execution, or portfolio management. The statistical techniques (Poisson regression, multi-state models) are actuarial in nature rather than financial market modeling.

Implementation requires: (1) restructuring multi-state transition data into Poisson regression format with exposure offsets and age-constant intervals, (2) fitting M separate Poisson regressions, (3) computing transition probabilities via matrix exponentials and Chapman-Kolmogorov equations, (4) applying fairness adjustments at the transition rate level, and (5) deriving premiums from adjusted rates. The adversarial debiasing variant adds neural network training complexity. Data restructuring (Appendix A) is particularly intricate.

The paper uses publicly available HRS data and provides detailed procedural descriptions including data restructuring steps (Appendix A), model selection details (Appendix B), and likelihood contributions (Appendix C). However, no code repository is provided, and the case study relies on specific data transformations that would require careful implementation. The R glm routine is referenced for estimation.