KEYNOTE: Dynamically Updating Motor Insurance Prices with Telematics Collected Driving Behavior Data
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 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. Throughout the talk we demonstrate the use of analytics for claim frequency, severity and churn modelling. We demonstrate the use of GBMs, GLMs and feature selection with lasso penalties. The talk is based on joint work with Dr Roel Henckaerts.