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

Allen R. Williams, Yoolim Jin, Anthony Duer, Tuka Alhani and Mohammad Ghassemi
Additional contact information
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

Risks, 2022, vol. 10, issue 6, 1-17

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.

Keywords: telematics; usage based insurance; risk mitigation (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-9091/10/6/118/pdf (application/pdf)
https://www.mdpi.com/2227-9091/10/6/118/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jrisks:v:10:y:2022:i:6:p:118-:d:833588

Access Statistics for this article

Risks is currently edited by Mr. Claude Zhang

More articles in Risks from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().