Unraveling the predictive power of telematics data in car insurance pricing

Roel Verbelen, Katrien Antonio and Gerda Claeskens

No 552745, Working Papers Department of Accountancy, Finance and Insurance (AFI), Leuven from KU Leuven, Faculty of Economics and Business (FEB), Department of Accountancy, Finance and Insurance (AFI), Leuven

Abstract: 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, 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 behavior of the policyholder. We develop a statistical modeling 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 discriminating rating variable redundant.

Keywords: Pay-as-you-drive insurance; Usage-based insurance; Risk classification; Generalized additive models; Compositional predictors; Structural zeros (search for similar items in EconPapers)
Date: 2016-10
New Economics Papers: this item is included in nep-ias and nep-tre
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Published in FEB Research Report KBI_1624

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Related works:
Journal Article: Unravelling the predictive power of telematics data in car insurance pricing (2018)
Working Paper: Unraveling the predictive power of telematics data in car insurance pricing (2018)
Working Paper: Unraveling the predictive power of telematics data in car insurance pricing (2018)
Working Paper: Unraveling the predictive power of telematics data in car insurance pricing (2016)
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