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In recent years it has become possible to collect GPS data from drivers andto incorporate this data into automobile insurance pricing for the driver. Thisdata is continuously collected and processed nightly into metadata consistingof mileage and time summaries of each discrete trip taken, and a set ofbehavioral scores describing attributes of the trip (e.g, driver fatigue ordriver distraction) so we examine whether it can be used to identify periods ofincreased risk by successfully classifying trips that occur immediately beforea 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 itcreates an opportunity for intervention and, potentially, avoidance of a claim.We examine metadata for each trip a driver takes and train a classifier topredict whether \textit{the following trip} is one in which a claim occurs forthat driver. By achieving a area under the receiver-operator characteristicabove 0.6, we show that it is possible to predict claims in advance.Additionally, we compare the predictive power, as measured by the area underthe receiver-operator characteristic of XGBoost classifiers trained to predictwhether a driver will have a claim using exposure features such as drivenmiles, and those trained using behavioral features such as a computed speedscore.

Nightly Automobile Claims Prediction from Telematics-Derived Features: A Multilevel Approach