Fairness of Ratemaking for Catastrophe Insurance: Lessons from Machine Learning

Nan Zhang () and Heng Xu ()
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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

Information Systems Research, 2024, vol. 35, issue 2, 469-488

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.

Keywords: catastrophe insurance; machine learning; fairness (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (2)

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