Covariate Selection from Telematics Car Driving Data

Mario V. Wuthrich

RiskLab, ETH Zurich; Swiss Finance Institute

December 19, 2016

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.

Number of Pages in PDF File: 18

Keywords: telematics data, driving habits, driving styles, regression, categorical classes, pattern recognition, clustering, K-means clustering, unsupervised learning, machine learning

JEL Classification: G22, G28

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Date posted: December 19, 2016  

Suggested Citation

Wuthrich, Mario V., Covariate Selection from Telematics Car Driving Data (December 19, 2016). Available at SSRN: https://ssrn.com/abstract=2887357 or http://dx.doi.org/10.2139/ssrn.2887357

Contact Information

Mario V. Wuthrich (Contact Author)
RiskLab, ETH Zurich ( email )
Department of Mathematics
Ramistrasse 101
Zurich, 8092
Swiss Finance Institute

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