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With the advancement in technology, telematics data which capture vehiclemovements information are becoming available to more insurers. As these datacapture the actual driving behaviour, they are expected to improve ourunderstanding of driving risk and facilitate more accurate auto-insuranceratemaking. In this paper, we analyze an auto-insurance dataset with telematicsdata collected from a major European insurer. Through a detailed discussion ofthe telematics data structure and related data quality issues, we elaborate onpractical challenges in processing and incorporating telematics information inloss modelling and ratemaking. Then, with an exploratory data analysis, wedemonstrate the existence of heterogeneity in individual driving behaviour,even within the groups of policyholders with and without claims, which supportsthe study of telematics data. Our regression analysis reiterates the importanceof telematics data in claims modelling; in particular, we propose a speedtransition matrix that describes discretely recorded speed time series andproduces statistically significant predictors for claim counts. We concludethat large speed transitions, together with higher maximum speed attained,nighttime driving and increased harsh braking, are associated with increasedclaim counts. Moreover, we empirically illustrate the learning effects indriving behaviour: we show that both severe harsh events detected at a highthreshold and expected claim counts are not directly proportional with drivingtime or distance, but they increase at a decreasing rate.

Data Mining of Telematics Data: Unveiling the Hidden Patterns in Driving Behaviour