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Cited by 126Traffic and Road SafetyProbability and Risk ModelsInsurance and Financial Risk Management

Unravelling the predictive power of telematics data in car insurance pricing

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Verbelen, Roel, Antonio, Katrien, Claeskens, Gerda·2018·Journal of the Royal Statistical Society Series C: Applied Statistics
DOI 10.1111/rssc.12283
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Abstract

Summary: A data set from a Belgian telematics product aimed at young drivers is used to identify how car insurance premiums can be designed based on the telematics data collected by a black box installed in the vehicle. In traditional pricing models for car insurance, the premium depends on self-reported rating variables (e.g. age and postal code) which capture characteristics of the policy(holder) and the insured vehicle and are often only indirectly related to the accident risk. Using telematics technology enables tailor-made car insurance pricing based on the driving behaviour of the policyholder. We develop a statistical modelling approach using generalized additive models and compositional predictors to quantify and interpret the effect of telematics variables on the expected claim frequency. We find that such variables increase the predictive power and render the use of gender as a rating variable redundant.

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  • Generalized Additive Models can overcome the linear predictor constraint of GLMs but have difficulty capturing the complex interactions among feature variables.

    “Alternative approaches using Generalized Additive Models (GAM) can overcome the linear predictor constraint of GLMs but have difficulty capturing the complex interactions among feature variables (Verbelen et al. 2018).”

    From Modelling Motor Insurance Claim Frequency and Severity Using Gradient Boosting (2023) · cites this paper as Verbelen et al. 2018

    Section 4.1 says GAMs model continuous predictors more flexibly than traditional GLMs, supporting the first part of the claim. Section 4.3 says the authors restricted model selection to additive terms to keep the search feasible and effects interpretable, but Section 5.2 says they also explored interactions. The paper does not establish that GAMs generally have difficulty capturing complex interactions. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • Gender differences in the risk of accidents are, to a large extent, attributable to the fact that men drive longer average distances than women.

    “They all concluded that Gender differences in the risk of accidents are, to a large extent, attributable to the fact that men drive longer average distances than women.”

    From The Use of Telematics Devices to Improve Automobile Insurance Rates (2019) · cites this paper as Verbelen, Antonio, & Claeskens, 2018

    The Policy variables section reports that women in the studied portfolio drove fewer kilometres per year than men and that the apparent gender difference in risk could be explained by driving habits. The Conclusion says that distance and how driving is distributed across categories take over part of gender's predictive power; it does not establish that longer average distance alone explains the difference to a large extent. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • Men on average drive significantly longer distances than women.

    “As found by other authors, the influence of Gender is masked by the fact that men on average drive significantly longer distances than women.”

    From The Use of Telematics Devices to Improve Automobile Insurance Rates (2019) · cites this paper as Verbelen, Antonio, & Claeskens, 2018

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Authors
Verbelen, R., Antonio, K., & Claeskens, G.
Affiliations
  • KU Leuven (BE)
  • University of Amsterdam (NL)
Corresponding author
Verbelen, Roel
ORCID
Venue
Journal of the Royal Statistical Society Series C: Applied Statistics
Publisher
Oxford University Press (OUP)
Published
2018-04-19
Volume
67
Issue
5
Pages
1275-1304
ISSN
  • 0035-9254 (ISSN-L)
  • 1467-9876
DOI
10.1111/rssc.12283
OpenAlex
W3124322175
Cited by
126
References
1
Citations by year
2026: 3 · 2025: 13 · 2024: 26 · 2023: 15 · 2022: 16 · 2021: 13 · 2020: 20 · 2019: 12 · 2018: 8
FWCI
16.05
Type
journal article
Language
en
Open access
Licence
academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
Locations
  • Journal of the Royal Statistical Society Series C (Applied Statistics) (publishedVersion)
  • UvA-DARE (University of Amsterdam) (publishedVersion, cc-by-nc)
  • Lirias (acceptedVersion, cc-by)
  • Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
Funders
  • Vlaams Supercomputer Centrum
  • KU Leuven (GOA/12/14, COMPACT C24/15/001)
Topics
  • Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Traffic and Road Safety
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Probability and Risk Models
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Insurance and Financial Risk Management
Keywords
telematics, black box, young drivers, driver behavior, claim frequency, generalized additive models, crash risk

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