On a strategy to develop robust and simple tariffs from motor vehicle insurance data

Abstract

The goals of this paper are twofold: we describe common features in data sets from motor vehicle insurance companies and we investigate a general strategy which exploits the knowledge of such features. The results of the strategy are a basis to develop insurance tariffs. The strategy is applied to a data set from motor vehicle insurance companies. We use a nonparametric approach based on a combination of kernel logistic regression and ¡support vector regression. --Classification,Data Mining,Insurance tariffs,Kernel logistic regression,Machine learning,Regression,Robustness,Simplicity,Support Vector Machine,Support Vector Regression