Balance and Fairness through Multicalibration in Nonlife Insurance Pricing

TL;DR

This paper explores multicalibration in nonlife insurance pricing, aiming to ensure fair and accurate premiums across groups by combining autocalibration with fairness considerations, demonstrated through practical methods and a real case study.

Contribution

It introduces multicalibration as a method to achieve both fairness and autocalibration in insurance pricing, with practical implementation strategies and a case study.

Findings

Multicalibration improves fairness in insurance premiums.

Practical methods include local regression and bias correction.

Case study shows effectiveness in motor insurance data.

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.