The Fair Pricing Playbook: Responsible AI for Algorithmic Insurance Pricing

Advances in AI and data science are transforming insurance pricing, enabling more accurate risk assessment, increasingly granular pricing, and improved customer outcomes. At the same time, these advances raise important questions about fairness, transparency, and accountability. Complex predictive models can inadvertently introduce indirect discrimination through data and algorithmic design, creating tensions between actuarial principles, regulatory requirements, and societal expectations. Regulators around the world are responding. For example, the EU AI Act classifies insurance risk assessment as a high-risk AI application, while jurisdictions including Colorado and New York are introducing requirements for insurers to assess pricing algorithms for unfair discrimination.

This session introduces The Fair Pricing Playbook ( https://fair.feihuang.org ), an open-source practical framework for developing, evaluating, and auditing algorithmic insurance pricing systems. The playbook translates recent advances in actuarial science, economics, statistics, and machine learning into a practical four-step workflow: defining an appropriate fairness objective, developing a pricing model that satisfies it, evaluating the resulting trade-offs for consumers and insurers, and auditing deployed pricing systems. Drawing on recent research in fairness metrics, anti-discrimination insurance pricing, fairness testing, and welfare analysis, the session demonstrates how responsible AI principles can be integrated into modern pricing practice through practical examples and open-source tools. It discusses the implications for the actuarial profession as AI regulation and industry expectations continue to evolve.

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