We present a dynamic pay-how-you-drive pricing scheme for motor insurance using telematics signals. More specifically, our approach allows the insurer to apply penalties to a baseline premium on the occurrence of events such as hard acceleration or braking. In addition, we incorporate a bonus-malus system (BMS) adapted for telematics data, providing a credibility component based on past telematics signals to the claim frequency predictions. We purposefully consider a weekly setting for our ratemaking approach to benefit from the signal’s high-frequency rate and to encourage safe driving via dynamic premium corrections. Moreover, we provide a detailed structure that allows our model to benefit from historical records and detailed telematics data collected weekly through an onboard device. We showcase our results numerically in a case study using data from an insurance company.

Previous research has found that people consult closeness or proximity cues when they evaluate the plausibility or likelihood of a counterfactual alternative to reality. In this paper we asked whether the plausibility of counterfactuals extends to dynamic proximity cues that signal a sense of propensity or acceleration in the lead-up to an outcome. Subjects gambled on obtaining either three heads or three tails from three coin-flips. When they lost the gamble they thought it was more likely that they could have won if they had lost on the third coin-flip that was revealed rather than the first or second coin-flip. We discuss how the sense of propensity was raised prior to the revelation of the final decisive losing coin-flip which created a perception of psychological momentum towards winning. Moreover, the consequence of this propensity effect was to positively bias perceptions of the likelihood of the counterfactual winning outcome.