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Covariate selection from telematics car driving data

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Abstract

Car insurance companies have started to collect high-frequency GPS location data of their car drivers. This data provides detailed information about the driving habits and driving styles of individual car drivers. We illustrate how this data can be analyzed using techniques from pattern recognition and machine learning. In particular, we describe how driving styles can be categorized so that they can be used for a regression analysis in car insurance pricing.

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Authors and Affiliations

  1. RiskLab, Department of Mathematics, ETH Zurich, 8092, Zurich, Switzerland

    Mario V. Wüthrich

  2. Swiss Finance Institute, Zurich, Switzerland

    Mario V. Wüthrich

Authors
  1. Mario V. Wüthrich
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Correspondence to Mario V. Wüthrich.

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Wüthrich, M.V. Covariate selection from telematics car driving data. Eur. Actuar. J. 7, 89–108 (2017). https://doi.org/10.1007/s13385-017-0149-z

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  • DOI: https://doi.org/10.1007/s13385-017-0149-z

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