Reports from Michigan State University Add New Study Findings to Research in Risk Management (Nightly Automobile Claims Prediction from Telematics-Derived Features: A Multilevel Approach): Insurance – Risk Management

2022 JUL 12 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- A new study on risk management is now available. According to news reporting out of East Lansing, Michigan, by NewsRx editors, research stated, “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.”

Our news editors obtained a quote from the research from Michigan State University: “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.”

According to the news editors, the research concluded: “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.”

For more information on this research see: Nightly Automobile Claims Prediction from Telematics-Derived Features: A Multilevel Approach. Risks, 2022,10(118):118. (Risks - http://www.mdpi.com/journal/risks). The publisher for Risks is MDPI AG.

A free version of this journal article is available at https://doi.org/10.3390/risks10060118.

Our news journalists report that additional information may be obtained by contacting Allen R. Williams, Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48842, United States. Additional authors for this research include Yoolim Jin, Anthony Duer, Tuka Alhani, Mohammad Ghassemi.

(Our reports deliver fact-based news of research and discoveries from around the world.)