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Fairness of Ratemaking for Catastrophe Insurance: Lessons from Machine Learning

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  • Nan Zhang

    (Warrington College of Business, University of Florida, Gainesville, Florida 32611)

  • Heng Xu

    (Kogod School of Business, American University, Washington, District of Columbia 20016)

Abstract

Catastrophe insurance is an important element of disaster management. Yet the historical presence of inequalities in insurance, from redlining to pricing disparity, has had a devastating impact on minority communities. Whereas the fairness of insurance ratemaking is studied in general, we identify a unique challenge for catastrophe insurance that sets it apart from other lines of insurance. Drawing upon the recent advances in machine learning for fair data valuation, we reveal striking connections between the two seemingly unrelated problems and lean on insights from machine learning to mathematically and empirically study the fairness of ratemaking methods for catastrophe insurance. Our results indicate the potential existence of disparate impact against minorities across existing methods and point to a unique mathematical solution that can satisfy a few commonly assumed properties of fair ratemaking for catastrophe insurance.

Suggested Citation

  • Nan Zhang & Heng Xu, 2024. "Fairness of Ratemaking for Catastrophe Insurance: Lessons from Machine Learning," Information Systems Research, INFORMS, vol. 35(2), pages 469-488, June.
    • Handle: RePEc:inm:orisre:v:35:y:2024:i:2:p:469-488
    DOI: 10.1287/isre.2022.1195
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    2. Ahmed Abbasi & Robin Dillon & H. Raghav Rao & Olivia R. Liu Sheng, 2024. "Preparedness and Response in the Century of Disasters: Overview of Information Systems Research Frontiers," Information Systems Research, INFORMS, vol. 35(2), pages 460-468, June.

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