Autocalibration is a desirable property, intimately related to the method of marginal totals that predates modern risk classification methods. Autocalibrated mean predictors π for a response Y and feature variables X are such that π(X) =E[Y |π(X)]. Autocalibration is beneficial since it ensures that the information contained in π is used without any systematic errors. Or stated differently, an autocalibrated predictor is optimal with respect to the information contained in it. Autocalibration can easily be implemented using the practical method proposed by Denuit, Charpentier and Trufin (2021), consisting in an extra local regression step. Professional practice favors Lorenz curves and Gini coefficients to assess the performances of a candidate premium. Under autocalibration, Denuit and Trufin (2021) established that the advanced diagnostic tools proposed by Denuit, Sznajder and Trufin (2019) reduce to Lorenz curve and Gini coefficients, respectively. The present note aims to assess the impact of autocalibration on the goodness of lift. Lift graphs compare average predicted and average actual loss costs when policies have been ranked according to the candidate premium and grouped within buckets. It is shown on a case study that autocalibration does not only restore global and local balances but also improve lift.

Keywords: Risk classification, Ratemaking, Lift curve, Goodness of lift, Machine learning.