On a Strategy to Develop Robust and Simple Tariffs from Motor Vehicle Insurance Data
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Date
2004
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Publisher
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