“Fairness of Ratemaking for Catastrophe Insurance: Lessons from Machine Learning”, an article co-authored by Nan Zhang and Heng Xu, was recently accepted for publication at Information Systems Research. 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 the article, we drew from the recent advances in machine learning to mathematically and empirically study the fairness of ratemaking methods for catastrophe insurance.

We will post a link to the article once it is published online.