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. In particular, we present a new characterization of autocalibration which enables to identify whether a predictor is autocalibrated or not, we study a method (called balance correction) for obtaining an autocalibrated predictor from any regression model, we highlight the effect of balance correction on resulting pure premiums, and finally we go trough some performances criteria that are particularly relevant for autocalibrated predictors.

Speaker

Julien Trufin (Université Libre de Bruxelles)