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

Andreas Christmann

No 2004,16, Technical Reports from Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen

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.

Keywords: Classification; Data Mining; Insurance tariffs; Kernel logistic regression; Machine learning; Regression; Robustness; Simplicity; Support Vector Machine; Support Vector Regression (search for similar items in EconPapers)
Date: 2004
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:sfb475:200416

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