🤖 AI Summary
This study addresses the dual challenge of actuarial fairness and group equity in non-life insurance pricing, which is essential to mitigate legal and reputational risks. It introduces the multi-calibration framework—originally developed in algorithmic fairness—to the insurance domain, unifying self-calibration and conditional mean independence as coherent fairness criteria. The proposed approach ensures that, within each premium group, average claims match expected revenues while simultaneously satisfying formal fairness guarantees. To operationalize this framework, the authors develop a practical pricing model incorporating local regression, within-group bias correction, and credibility adjustments to enforce multi-calibration constraints. Empirical analysis using real-world auto insurance data demonstrates that the method effectively balances financial soundness with equitable treatment across demographic groups, offering a viable pathway toward fair and sustainable insurance pricing.
📝 Abstract
Autocalibration is known to be an important requirement for insurance premiums since it guarantees that premium income balances corresponding claims, on average, not only at portfolio level but also inside each group paying similar premiums. Also, fairness has become a major concern because unfair treatment may expose insurers to lawsuits or reputational damage. Translating fairness into conditional mean independence allows actuaries to combine autocalibration and fairness into the multicalibration concept. This paper studies the properties of multicalibration in an insurance context and proposes practical ways to implement it, through local regression or bias correction within groups including credibility adjustments. A case study based on motor insurance data illustrates the relevance of multicalibration in insurance pricing.