European Actuarial Journal

, Volume 7, Issue 1, pp 89–108 | Cite as

Covariate selection from telematics car driving data

Original Research Paper
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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.

Keywords

Telematics data Driving habits Driving styles Regression Categorical classes Pattern recognition Clustering K-means clustering Unsupervised learning Machine learning. 

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Copyright information

© EAJ Association 2017

Authors and Affiliations

  1. 1.RiskLab, Department of MathematicsETH ZurichZurichSwitzerland
  2. 2.Swiss Finance InstituteZurichSwitzerland

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