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. In an article forthcoming at Information Systems Research, we drew from the recent advances in machine learning to mathematically and empirically study the fairness of ratemaking methods for catastrophe insurance.

The article, “Fairness of Ratemaking for Catastrophe Insurance: Lessons from Machine Learning”, was co-authored by Nan Zhang and Heng Xu.