Improving automobile insurance ratemaking using telematics: incorporating mileage and driver behaviour data

Abstract

We show how data collected from a GPS device can be incorporated in motor insurance ratemaking. The calculation of premium rates based upon driver behaviour represents an opportunity for the insurance sector. Our approach is based on count data regression models for frequency, where exposure is driven by the distance travelled and additional parameters that capture characteristics of automobile usage and which may affect claiming behaviour. We propose implementing a classical frequency model that is updated with telemetrics information. We illustrate the method using real data from usage-based insurance policies. Results show that not only the distance travelled by the driver, but also driver habits, significantly influence the expected number of accidents and, hence, the cost of insurance coverage. Telemetry should facilitate the inclusion within insurance pricing of those factors that traffic authorities identify as being associated with risky drivers, including, for example, traffic violations.