Sustainable insurance pricing requires that premiums collected cover the losses generated by policyholders, insurer expenses and provide an adequate rate of return. The two mechanisms for allocating these costs across policyholders are social pricing and risk-based pricing.

In implementing risk-based insurance pricing, insurers set premiums based on observable characteristics that are correlated with loss experience and group drivers into categories, charging the same base premium within a category. Even in jurisdictions with social pricing, an accurate predictor of an insured’s future losses is past driving history. Insurers use experience rating to assign drivers to risk classes where the premium reflects the driver’s past driving history.

Experience rating cannot be used for novice drivers because they have no driving history, and how jurisdictions price insurance for these drivers may create availability or affordability problems. In many jurisdictions, the premium charged for novice drivers is not distinguished from the premium charged for higher-risk drivers. Other jurisdictions have developed mechanisms to promote affordability. Examples include lower state-prescribed rates for novice drivers, risk-sharing pools for novice drivers and increasing the number of driver risk categories. We summarize these different mechanisms that are commonly used in North America, Europe and Australia.

We examine the impact of these different rating mechanisms on novice drivers. We use stochastic modeling to abstract from jurisdiction-specific policy provisions models. Drivers in the system are assigned a constant annual at-fault accident rate intensity drawn from a distribution of accident frequencies and move between the driver risk categories according to their driving history. Transition probabilities between the states arise from the movement of drivers according to their claims histories and the experience rating mechanism. We calibrate our model using data on accident frequency by driver risk class and by numbers of years licensed collected by the General Insurance Statistical Agency in Canada. Using a constant severity per claim, we derive the expected losses for the entire portfolio of drivers and for each driver risk category. For each experience rating mechanism, we calculate the actuarially fair base premium and the driver risk class differentials. We then compare the different systems with respect to fairness to both novice and experienced drivers.

From a policy perspective, the point to emphasize is that mechanisms that promote affordability should not lessen incentives for safe driving but should strengthen the responsiveness of insurance premiums to driving history. This will reduce moral hazard and adverse selection.