  Powered with telematics technology, insurers can now capture a wide range of
data, such as distance traveled, how drivers brake, accelerate or make turns,
and travel frequency each day of the week, to better decode driver's behavior.
Such additional information helps insurers improve risk assessments for
usage-based insurance (UBI), an increasingly popular industry innovation. In
this article, we explore how to integrate telematics information to better
predict claims frequency. For motor insurance during a policy year, we
typically observe a large proportion of drivers with zero claims, a less
proportion with exactly one claim, and far lesser with two or more claims. We
introduce the use of a cost-sensitive multi-class adaptive boosting (AdaBoost)
algorithm, which we call SAMME.C2, to handle such imbalances. To calibrate
SAMME.C2 algorithm, we use empirical data collected from a telematics program
in Canada and we find improved assessment of driving behavior with telematics
relative to traditional risk variables. We demonstrate our algorithm can
outperform other models that can handle class imbalances: SAMME, SAMME with
SMOTE, RUSBoost, and SMOTEBoost. The sampled data on telematics were
observations during 2013-2016 for which 50,301 are used for training and
another 21,574 for testing. Broadly speaking, the additional information
derived from vehicle telematics helps refine risk classification of drivers of
UBI.
