Fairness of Ratemaking for Catastrophe Insurance: Lessons from Machine Learning

• 成果类型: Article
• 署名作者: Zhang, Nan; Xu, Heng
• 署名单位: State University System of Florida; University of Florida; American University
• 刊物名称: INFORMATION SYSTEMS RESEARCH
• ISSN/ISSBN: 1047-7047
• DOI: 10.1287/isre.2022.1195
• 发表日期: 2024
• 页码: 469-488
• 关键词: Shapley value; Data breach; cost allocation; RISK; HEALTH; vulnerability; FOUNDATIONS; valuation; ECONOMICS; PROPERTY

摘要

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.