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

Abstract

In recent years it has become possible to collect GPS data from drivers and to incorporate these data into automobile insurance pricing for the driver. These data are 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 the following trip is one in which a claim occurs for that driver. By achieving an 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.

Weather Conditions and Telematics Panel Data in Monthly Motor Insurance Claim Frequency Models

“…Specifically, speed, road type, and the time of day when the vehicle is used seem to have a strong relationship with the number of claims, and alternative models have been proposed in the literature to describe the joint dynamics of telematics data and claim occurrence (Ayuso et al 2014;Guillen et al 2019;Corradin et al 2022, among others). Moreover, such detailed telematics information can be used to identify periods of increased risk by classifying trips that occur immediately before a claim (see Williams et al 2022). Eling and Kraft (2020) reviewed over fifty papers published in the last two decades on usage-based insurance, with a summary of the methods, the selected telematics variables, and the key findings.…”