On a Strategy to Develop Robust and Simple Tariffs from Motor Vehicle Insurance Data

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Date

2004

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Universitätsbibliothek Dortmund

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 e-support vector regression.

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Keywords

data mining, insurance tariffs, kernel logistic regression, machine learning, regression, robustness, simplicity, support vector machine, support vector regression

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