A1457
Title: Autocalibrated predictors in nonlife insurance pricing
Authors: Michel Denuit - Universite catholique de Louvain (Belgium)
Julien Trufin - Université Libre de Bruxelles (Belgium) [presenting]
Abstract: By exploiting massive amounts of data, 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. Balance correction has been proposed as a way to make any candidate premium autocalibrated with the added advantage that it improves out-of-sample Bregman divergence and, hence, predictive Tweedie deviance. The aim is to prove that balance correction is also beneficial in terms of concentration curves and conditions being derived, ensuring that the initial predictor and its balance-corrected version are ordered in Lorenz order. Finally, criteria are proposed to rank the balance-corrected versions of two competing predictors in the convex order.
