  In recent years it has become possible to collect GPS data from drivers and
to incorporate this data into automobile insurance pricing for the driver. This
data is continuously collected and processed nightly into metadata consisting
of mileage and time summaries of each discrete trip taken, and a set of
behavioral scores describing attributes of the trip (e.g, driver fatigue or
driver distraction) so we examine whether it can be used to identify periods of
increased risk by successfully classifying trips that occur immediately before
a trip in which there was an incident leading to a claim for that driver.
Identification of periods of increased risk for a driver is valuable because it
creates an opportunity for intervention and, potentially, avoidance of a claim.
We examine metadata for each trip a driver takes and train a classifier to
predict whether \textit{the following trip} is one in which a claim occurs for
that driver. By achieving a area under the receiver-operator characteristic
above 0.6, we show that it is possible to predict claims in advance.
Additionally, we compare the predictive power, as measured by the area under
the receiver-operator characteristic of XGBoost classifiers trained to predict
whether a driver will have a claim using exposure features such as driven
miles, and those trained using behavioral features such as a computed speed
score.
