Learning Fair Decisions with Factor Models: Applications to Annuity Pricing

TL;DR

This paper introduces a Fair Decision Model for annuity pricing that incorporates fairness regularization to reduce bias across demographic groups, improving equity and accuracy in high-stakes insurance decisions.

Contribution

The paper proposes a novel fairness regularization method for factor models, specifically targeting decision error parity in annuity pricing applications.

Findings

Significantly reduces decision error disparity across groups

Improves predictive accuracy over benchmark models

Demonstrates effectiveness on Australian mortality data

Abstract

Fairness-aware statistical learning is essential for mitigating discrimination against protected attributes such as gender, race, and ethnicity in data-driven decision-making. This is particularly critical in high-stakes applications like insurance underwriting and annuity pricing, where biased business decisions can have significant financial and social consequences. Factor models are commonly used in these domains for risk assessment and pricing; however, their predictive outputs may inadvertently introduce or amplify bias. To address this, we propose a Fair Decision Model that incorporates fairness regularization to mitigate outcome disparities. Specifically, the model is designed to ensure that expected decision errors are balanced across demographic groups - a criterion we refer to as Decision Error Parity. We apply this framework to annuity pricing based on mortality modelling. An empirical analysis using Australian mortality data demonstrates that the Fair Decision Model can significantly reduce decision error disparity while also improving predictive accuracy compared to benchmark models, including both traditional and fair factor models.