  With the advancement in technology, telematics data which capture vehicle
movements information are becoming available to more insurers. As these data
capture the actual driving behaviour, they are expected to improve our
understanding of driving risk and facilitate more accurate auto-insurance
ratemaking. In this paper, we analyze an auto-insurance dataset with telematics
data collected from a major European insurer. Through a detailed discussion of
the telematics data structure and related data quality issues, we elaborate on
practical challenges in processing and incorporating telematics information in
loss modelling and ratemaking. Then, with an exploratory data analysis, we
demonstrate the existence of heterogeneity in individual driving behaviour,
even within the groups of policyholders with and without claims, which supports
the study of telematics data. Our regression analysis reiterates the importance
of telematics data in claims modelling; in particular, we propose a speed
transition matrix that describes discretely recorded speed time series and
produces statistically significant predictors for claim counts. We conclude
that large speed transitions, together with higher maximum speed attained,
nighttime driving and increased harsh braking, are associated with increased
claim counts. Moreover, we empirically illustrate the learning effects in
driving behaviour: we show that both severe harsh events detected at a high
threshold and expected claim counts are not directly proportional with driving
time or distance, but they increase at a decreasing rate.
