[2606.14898] α-Fair Insurance Pricing: A Fairness Continuum
[Submitted on 12 Jun 2026]
Title:α-Fair Insurance Pricing: A Fairness Continuum
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Abstract:Fairness in insurance pricing remains a long-standing and deeply debated puzzle. On one hand, insurers, driven by profitability considerations, set premiums that differentiate across individual risks to achieve actuarial fairness. On the other hand, insurance serves a critical societal function by pooling risks across a population, motivating cross-subsidization among groups to promote solidarity fairness. The tension between these two competing notions of fairness makes insurance pricing inherently complex, particularly in modern settings where granular data allow for increasingly fine risk differentiation and regulators face growing pressure to protect vulnerable groups. To address this challenge, we propose an $\alpha$-\textbf{F}air \textbf{I}ndividual \textbf{S}olvent \textbf{P}remium ($\alpha$-FISP) framework for insurance pricing that explicitly captures the trade-off between actuarial and solidarity fairness while guaranteeing solvency, a fundamental requirement in insurance operations. We formulate the pricing problem as a constrained optimization task, where actuarially fair premiums are adjusted subject to budget constraints on cross-subsidization within each risk class. This formulation naturally yields a family of solutions parameterized by $\alpha$, tracing a continuum between purely actuarial and purely solidarity-based pricing and enabling decision-makers to select an operating point along this fairness spectrum. We derive theoretical guarantees for the proposed framework. Numerical experiments show that $\alpha$-FISP is computationally tractable and aligns well with the U.S. regulatory regimes featuring heterogeneous state-level fairness requirements.
Subjects:
Machine Learning (cs.LG)
Cite as: arXiv:2606.14898 [cs.LG]
(or arXiv:2606.14898v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.14898
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Tianhe Zhang [view email] [v1] Fri, 12 Jun 2026 19:05:12 UTC (8,045 KB)
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