α-FISP framework balances actuarial and solidarity fairness in insurance pricing
A single parameter α controls the trade-off between risk differentiation and cross-subsidization.
Insurance pricing faces a fundamental tension: actuarial fairness prices individual risk accurately, while solidarity fairness pools risk across populations to protect vulnerable groups. The α-FISP framework resolves this by formulating premium setting as a constrained optimization problem. It adjusts actuarially fair premiums under budget constraints on cross-subsidization within each risk class. The solution family is parameterized by α, tracing a continuous spectrum from purely actuarial to purely solidarity-based pricing. This allows decision-makers to select any desired trade-off while guaranteeing insurer solvency.
The framework provides theoretical guarantees for the continuum approach. Numerical experiments confirm computational tractability and demonstrate strong alignment with U.S. regulatory regimes, which have heterogeneous state-level fairness requirements. By offering a mathematically rigorous and flexible tool, α-FISP enables insurers and regulators to navigate the complex fairness landscape without sacrificing operational viability. The work bridges machine learning, economics, and insurance regulation, and has potential applications beyond insurance to other domains requiring fairness-constrained pricing.
- Framework uses constrained optimization to adjust premiums under budget constraints on cross-subsidization.
- Parameter α allows decision-makers to choose any point on the fairness spectrum between actuarial and solidarity.
- Numerical experiments demonstrate tractability and compliance with heterogeneous U.S. state-level fairness rules.
Why It Matters
Enables insurers and regulators to mathematically balance fairness and solvency, adapting to diverse regional requirements.