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

Author

Listed:
  • Allen R. Williams

    (Department of Computer Science & Engineering, Michigan State University, East Lansing, MI 48842, USA)

  • Yoolim Jin

    (CSAA Insurance Group 3055 Oak Road, Walnut Creek, CA 94597, USA)

  • Anthony Duer

    (CSAA Insurance Group 3055 Oak Road, Walnut Creek, CA 94597, USA)

  • Tuka Alhani

    (Engineering Division, New York University Abu Dhabi, Saadiyat Campus, Abu Dhabi P.O. Box 129188, United Arab Emirates)

  • Mohammad Ghassemi

    (Department of Computer Science & Engineering, Michigan State University, East Lansing, MI 48842, USA)

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.

Suggested Citation

  • Allen R. Williams & Yoolim Jin & Anthony Duer & Tuka Alhani & Mohammad Ghassemi, 2022. "Nightly Automobile Claims Prediction from Telematics-Derived Features: A Multilevel Approach," Risks, MDPI, vol. 10(6), pages 1-17, June.
  • Handle: RePEc:gam:jrisks:v:10:y:2022:i:6:p:118-:d:833588
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    References listed on IDEAS

    as
    1. Mohamed Hanafy & Ruixing Ming, 2021. "Machine Learning Approaches for Auto Insurance Big Data," Risks, MDPI, vol. 9(2), pages 1-23, February.
    2. So, Banghee & Boucher, Jean-Philippe & Valdez, Emiliano A., 2021. "Cost-Sensitive Multi-Class Adaboost For Understanding Driving Behavior Based On Telematics," ASTIN Bulletin, Cambridge University Press, vol. 51(3), pages 719-751, September.
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