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

• URL: http://arxiv.org/abs/2205.04616v1
• Date: Tue, 10 May 2022 01:25:10 GMT
• Title: Nightly Automobile Claims Prediction from Telematics-Derived Features: A Multilevel Approach
• Authors: Allen R. Williams, Yoolim Jin, Anthony Duer, Tuka Alhanai, Mohammad Ghassemi
• Abstract summary: 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. 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.
• Score: 1.6799377888527685
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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