Covariate selection from telematics car driving data
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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.
KeywordsTelematics data Driving habits Driving styles Regression Categorical classes Pattern recognition Clustering K-means clustering Unsupervised learning Machine learning.
- 6.Wüthrich MV, Buser C (2016) Data analytics for non-life insurance pricing. Swiss Finance Institute Research Paper No. 16-68. Available at SSRN: https://ssrn.com/abstract=2870308