Many automobile insurance companies offer the possibility to monitor driving habits and distance driven by means of telematics devices installed in the vehicles. This provides a novel source of data that can be analysed to calculate personalised tariffs. For instance, drivers who accumulate a lot of miles should be charged more for their insurance coverage than those who make little use of their car. However, it can also be argued that drivers with more miles have better driving skills than those who hardly use their vehicle, meaning that the price per mile should decrease with distance driven. The statistical analysis of a real data set by means of machine learning techniques shows the existence of a gaining experience effect for large values of distance travelled, so that longer driving should result in higher premium, but there should be a discount for drivers that accumulate longer distances over time due to the increased proportion of zero claims. We confirm that speed limit violations and driving in urban areas increase the expected number of accident claims. We discuss how telematics information can be used to design better insurance and to improve traffic safety. Predictive models provide benchmarks of the impact of semi-autonomous vehicles on insurance rates. This talk will cover the award winning paper on semiautonomous vehicle insurance presented in the International Congress of Actuaries in Berlin, June, 2018, which is under revision in Accident Analysis and Prevention and it will also include the contents of a paper entitled “The use of telematics devices to improve automobile insurance rates”, accepted in Risk Analysis.
