On a Strategy to Develop Robust and Simple Tariffs from Motor Vehicle Insurance Data
On a Strategy to Develop Robust and Simple Tariffs from Motor Vehicle Insurance Data
摘要
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. We use a nonparametric approach based on a combination of kernel logistic regression and e-support vector regression which both have good robustness properties. The strategy is applied to a data set from motor vehicle insurance companies.
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. We use a nonparametric approach based on a combination of kernel logistic regression and e-support vector regression which both have good robustness properties. The strategy is applied to a data set from motor vehicle insurance companies.
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