Katrien Antonio

big data and econometrics seminar
à distance

Katrien Antonio

KU Leuven
Dynamically updating motor insurance prices with telematics collected driving behavior data
Co-écrit avec
Roel Henckaerts
Mardi 15 mars 2022| 14:00 - 15:30

Michel Lubrano : michel.lubrano[at]univ-amu.fr
Pierre Michel : pierre.michel[at]univ-amu.fr


We analyze a novel dataset collecting the driving behavior of young policyholders in a motor third party liability (MTPL) portfolio, followed over a period of three years. Driving habits are measured by the total mileage and the distance driven on different road types and during distinct time slots. Driving style is characterized by the number of harsh acceleration, braking, cornering and lateral movement events. First, we develop a baseline pricing model for the complete portfolio with claim history and self-reported risk characteristics of approximately 400 000 policyholders each year. Next, we propose a methodology to update the baseline price via the telematics information of young drivers. Our approach results in a truly usage-based insurance (UBI) product, making the premium dependent on a policyholder's driving habits and style. We highlight the added value of telematics via improvements in risk classification and we put focus on managerial insights by analyzing expected profits and retention rates under our new UBI pricing structure.

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