Pay-as-you-drive (PAYD), or usage-based automobile insurance (UBI), is a policy agreement tied to vehicle usage. In this paper we analyze the effect of the distance traveled on the risk of accidents among young drivers with a PAYD policy. We use regression models for survival data to estimate how long it takes them to have their first accident at fault during the coverage period. Our empirical application with real data is presented and shows that gender differences are mainly attributable to the intensity of use. Indeed, although gender has a significant effect in explaining the time to the first crash, this effect is no longer significant when the average distance traveled per day is introduced in the model. This suggests that gender differences in the risk of accidents are, to a large extent, attributable to the fact that men drive more often than women. Estimates of the time to the first accident for different driver risk types are presented. We conclude that no gender discrimination is necessary if telematics provides enough information on driving habits.

We present a method to integrate telematics data in a pay‐how‐you‐drive insurance pricing scheme that penalizes some near‐miss events. We illustrate our method with a sample of drivers for whom information on near‐miss events and claims frequency records are available. We discuss the implications for motor insurance ratemaking. Our pricing principle is to combine a baseline insurance premium with added extra charges for near‐miss events indicating risky driving (or discounts) that can be updated on a weekly basis. This procedure provides an incentive for safe driving. In our real‐case study illustration, hard‐braking and acceleration events as well as smartphone use while driving increase the cost of insurance.

Telematics data from usage-based motor insurance provide valuable information – including vehicle usage, attitude toward speeding, and time and proportion of urban/nonurban driving, which can be used for ratemaking. Additional information on acceleration, braking, and cornering can likewise be usefully employed to identify near-miss events, a concept taken from aviation that denotes a situation that might have resulted in an accident. We analyze near-miss events from a sample of drivers in order to identify the risk factors associated with a higher risk of near-miss occurrence. Our empirical application with a pilot sample of real usage-based insurance data reveals that certain factors are associated with a higher expected number of near-miss events, but that the association differs depending on the type of near miss. We conclude that nighttime driving is associated with a lower risk of cornering events, urban driving increases the risk of braking events, and speeding is associated with acceleration events. These results are relevant for the insurance industry in order to implement dynamic risk monitoring through telematics, as well as preventive actions.