Summary:
This paper describes common features in data sets from motor vehicle insurance companies and proposes a general approach which exploits knowledge of such features in order to model high–dimensional data sets with a complex dependency structure. The results of the approach can be a basis to develop insurance tariffs. The approach is applied to a collection of data sets from several motor vehicle insurance companies. As an example, we use a nonparametric approach based on a combination of two methods from modern statistical machine learning, i.e. kernel logistic regression and ε-support vector regression.
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*This work was supported by the Deutsche Forschungsgemeinschaft (SFB 475, “Reduction of complexity in multivariate data structures”) and by the Forschungsband Do-MuS from the University of Dortmund. I am grateful to Mr. A. Wolfstein and Dr. W. Terbeck from the Verband öffentlicher Versicherer in Düsseldorf, Germany, for making available the data set and for many helpful discussions.
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Christmann*, A. An approach to model complex high–dimensional insurance data. Allgemeines Statistisches Arch 88, 375– 396 (2004). https://doi.org/10.1007/s101820400178
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DOI: https://doi.org/10.1007/s101820400178
Keywords:
- Classification
- data mining
- insurance tariffs
- kernel logistic regression
- machine learning
- regression
- robustness
- simplicity
- support vector machine
- support vector regression