Research Paper Series on Race and Insurance Pricing

The CAS Research Paper Series on Race & Insurance Pricing was created by the Casualty Actuarial Society to help guide the insurance industry toward proactive, quantitative solutions to address potential racial bias in insurance pricing. The reports explore different aspects of unintentional potential bias in insurance pricing and address both historical foundations as well as forward-looking solutions for quantifying and addressing potential bias. With these reports, the CAS aims to inspire and generate discussions about potential bias across all areas of insurance pricing and other insurance operations, and to encourage actuaries to proactively participate in conversations with other stakeholders on this topic.

Navigating Fairness - 2024 Race in Reinsurance Series

A Practical Guide to Navigating Fairness in Insurance

By Jessica Leong, FCAS; Richard Moncher, FCAS; and Kate Jordan

This paper aims to create a framework to help insurers develop models that are more likely to comply with evolving regulations on unfair discrimination and bias.

Regulatory Perspectives on Algorithmic Bias and Unfair Discrimination

By Lauren Cavanaugh, FCAS, MAAA; Scott Merkord, FCAS, MAAA; Taylor Davis, FCAS, MAAA; and David Heppen, FCAS, MAAA

This paper aims to explore regulatory perspectives on algorithmic bias, including U.S. state regulator concerns with current insurance pricing practices, perceptions of fairness testing approaches and plans for future activities.

Balancing Risk Assessment and Social Fairness: An Auto Telematics Case Study

By Jean-Philippe Boucher, Ph.D. and Mathieu Pigeon, Ph.D.

This paper evaluates the potential for telematics or usage-based insurance rating variables to reduce insurers reliance on protected information, (e.g. sex, age), or sensitive information, (e.g. marital status, territory, credit).

Comparison of Regulatory Framework AI usage in Insurance

Comparison of Regulatory Framework for Non-Discriminatory AI Usage in Insurance.

By: David Schraub, FSA, CERA, MAAA, AQ; Jing Lang, FSA, FCIA, MAAA, FLMI; Zhibin Zhang, FSA, FCIA; and Mark A. Sayre, FSA, CERA, JD, CIPP/US|E

This joint report from the CAS and Society of Actuaries offers a comprehensive overview of the latest and emerging regulatory activities in China, the U.S., Canada, and Europe, focusing on the prevention of discriminatory practices in the use of artificial intelligence (AI) within the insurance industry. It has a particular relevance to our international audiences.

Now Available! Part 1 - Practical Application of Bias Measurement and Mitigation Techniques in Insurance Pricing

By CAS Race and Insurance Pricing Task Force

This two-part paper serves as a practitioners guide to applying fairness testing and mitigation methods to actuarial pricing data and models. Part 1 provides an overview of bias types in insurance, introduces imputation methods for protected class labels, and presents simple fairness tests based on established statistical fairness criteria, expanding upon the 2022 "Methods for Quantifying..." paper.

Now Available! Part 2 - Practical Application of Bias Measurement and Mitigation Techniques in Insurance Pricing

This two-part paper serves as a practitioners guide to applying fairness testing and mitigation methods to actuarial pricing data and models. Part 2 explores more advanced fairness tests that account for the complexities of insurance pricing data, explores bias mitigation techniques throughout the modeling process, and discusses non-modeling factors affecting fairness in insurance pricing.

Now Available! Potential Unintended Impacts of Bias Mitigation in a Competitive Insurance Market

By Gary Wang, FCAS, CSPA, MAAA and Michael K. Chen, FCAS, CSPA, MAAA

This paper investigates the balance between maintaining accurate risk differentiation and ensuring equitable treatment among various classes of interest when the use of certain rating variables is restricted or prohibited by insurance regulatory law. Using a collection of simulated scenarios, we review the impacts of a spectrum of regulatory approaches intended to improve fairness for a certain class of interest on both that class and an additional class of interest. We also examine the potential impact on a competitive market composed of multiple companies with varying portfolio mixes.

Phase I – Released in 2022

Methods for Quantifying Discriminatory Effects on Protected Classes in Insurance

Methods for Quantifying Discriminating Effects

By Roosevelt Mosley, FCAS, CSPA and Radost Wenman, FCAS

As the insurance industry focuses attention on potential racial bias across all practice areas, this paper examines three approaches to defining and measuring fairness in predictive models. It also provides an overview of several bias mitigation techniques that can be performed during the input, modeling, or output phase of a model once a set of fairness criteria has been adopted.

Approaches to Address Racial Bias in Financial Services: Lessons for the Insurance Industry

By Members of the 2021 CAS Race and Insurance Research Task Force

This paper examines issues of racial bias in lending practice for mortgages, personal and commercial lending, as well as credit-scoring. It looks at these four areas and describes solutions intended to address any potential bias, which may include government intervention, internal bias testing and monitoring measures, and development of new products to mitigate bias.

Defining Discrimination in Insurance

By Kudakwashe F. Chibanda, FCAS

This paper defines several terms that are currently being used in discussions around potential discrimination in insurance – protected class, unfair discrimination, proxy discrimination, disparate impact, disparate treatment, and disproportionate impact – and provides historical and practical context for them. It also illustrates the inconsistencies in how different stakeholders define these terms.

Please see errata for revisions made on September 23, 2022.

Errata for Defining Discrimination in Insurance (September 23, 2022)

Understanding Potential Influences of Racial Bias on P&C Insurance: Four Rating Factors Explored

By Members of the 2021 CAS Race and Insurance Research Task Force

This paper examines four commonly used rating factors in personal lines insurance – credit-based insurance score, geographic location, home ownership, and motor vehicle record – to understand how the data underlying insurance pricing models may be impacted by racially biased policies and practices outside of the system of insurance.

Additional Resources

Continuing Education

Check out the Race and Insurance Bundle on UCAS for FREE session recordings from past meetings
Find Select recordings on the CAS YouTube Channel

CAS Research Papers

Access research produced by CAS members and staff related to Race and Insurance Issues:

Insurance Rating Variables: What They Are and Why They Matter
Research from the CAS E-Forum
- Considerations for Managing Potential Bias in Pricing Models

Collaboration on Industry Research

CAS appreciates the opportunity to partner with and review research performed by other insurance industry organizations.

National Association of Mutual Insurance Companies (NAMIC): Matching Rate to Risk: Analysis of the Availability and Affordability of Private Passenger Automobile Insurance

Resources from the American Academy of Actuaries

Issue Briefs:

Comment Letters:

Resources from the Canadian Institute of Actuaries

Bias and Fairness in Pricing and Underwriting of Property & Casualty Risks

Do you have feedback on the Race and Insurance Pricing Research Series or an idea for additional topics for research on this issue? The CAS research committees are interested in your input to potential research projects. Submit your idea or feedback in the box below and it will be forwarded to the appropriate CAS research committee. Please include your name and e-mail address so that you can be contacted for further discussion about your idea, if the need arises.

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