  Usage-based insurance is becoming the new standard in vehicle insurance; it
is therefore relevant to find efficient ways of using insureds' driving data.
Applying anomaly detection to vehicles' trip summaries, we develop a method
allowing to derive a "routine" and a "peculiarity" anomaly profile for each
vehicle. To this end, anomaly detection algorithms are used to compute a
routine and a peculiarity anomaly score for each trip a vehicle makes. The
former measures the anomaly degree of the trip compared to the other trips made
by the concerned vehicle, while the latter measures its anomaly degree compared
to trips made by any vehicle. The resulting anomaly scores vectors are used as
routine and peculiarity profiles. Features are then extracted from these
profiles, for which we investigate the predictive power in the claim
classification framework. Using real data, we find that features extracted from
the vehicles' peculiarity profile improve classification.
