Title: Unraveling the predictive power of telematics data in car insurance pricing
Authors: Verbelen, Roel
Antonio, Katrien
Claeskens, Gerda
Issue Date: Oct-2016
Publisher: KU Leuven - Faculty of Economics and Business
Series Title: FEB Research Report KBI_1624
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.
Publication status: published
KU Leuven publication type: IR
Appears in Collections:Research Center Insurance, Leuven
Research Center for Operations Research and Business Statistics (ORSTAT), Leuven

Files in This Item:
File Description Status SizeFormat
KBI_1624.pdfUnraveling the predictive power of telematics data in car insurance pricing Published 2637KbAdobe PDFView/Open


All items in Lirias are protected by copyright, with all rights reserved.