Unraveling the Predictive Power of Telematics Data in Car Insurance Pricing

45 Pages Posted: 18 Nov 2016  

Verbelen Roel

KU Leuven

Katrien Antonio

KU Leuven

Gerda Claeskens

Katholieke Universiteit Leuven (KUL)

Date Written: September 29, 2016

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

Suggested Citation

Roel, Verbelen and Antonio, Katrien and Claeskens, Gerda, Unraveling the Predictive Power of Telematics Data in Car Insurance Pricing (September 29, 2016). Available at SSRN: https://ssrn.com/abstract=2872112 or http://dx.doi.org/10.2139/ssrn.2872112

Verbelen Roel (Contact Author)

KU Leuven ( email )

Naamsestraat 69
B-3000 Leuven, Vlaams-Brabant 3000
Belgium

Katrien Antonio

KU Leuven ( email )

Oude Markt 13
Leuven, Vlaams-Brabant

Gerda Claeskens

Katholieke Universiteit Leuven (KUL) ( email )

Leuven, B-3000
Belgium

Paper statistics

Downloads
67
Rank
279,541
Abstract Views
137