Julien Trufin: Insurance Pricing and Financial Equilibrium through Autocalibration

Abstract

Machine learning techniques provide actuaries with predictors exhibiting high correlation with claim frequencies and severities. However, these predictors generally fail to achieve financial equilibrium and thus do not qualify as pure premiums. Autocalibration effectively addresses this issue since it ensures that every group of policyholders paying the same premium is on average self-financing. This talk proposes to look at recent results concerning autocalibration, namely (1) a new characterization of autocalibration, based on the graphs of Lorenz and Concentration curves, (2) the effect of balance correction on resulting pure premiums and (3) some performances criteria in the class of autocalibrated predictors.