Pricing weekly motor insurance drivers’ with behavioural and telematics data
Research presents compelling evidence that dynamic telematics factors offer easily interpretable and transparent algorithms that represent the future of dynamic driving safety assessment.
Telematics boxes integrated into vehicles a.re instrumental in capturing driving data that encompasses behavioural and contextual information, such as speed, distance travelled by road type, and time of day. This data can be amalgamated with drivers' individual attributes and reported accident occurrences to their respective insurance providers. This study analyses a substantial sample size of 19,214 individual drivers over a span of 55 weeks, covering a cumulative distance of 181.4 million kilometres driven. Utilising this dataset, the researchers develop predictive models for weekly accident frequency.
The findings of the study Pricing weekly motor insurance drivers’ with behavioral and contextual telematics data affirm that behavioural traits, such as instances of excessive speed, and contextual data pertaining to road type and time of day significantly aid in ratemaking design. The predictive models enable the creation of driving scores and personalised warnings, presenting a potential to enhance traffic safety by alerting drivers to perilous conditions.
The study delves into the construction of multiplicative scores derived from Poisson regression, contrasting them with additive scores resulting from a linear probability model approach, which offer greater communicability. Furthermore, the research demonstrates that the inclusion of lagged behavioural and contextual factors not only enhances prediction accuracy, it also lays the foundation for a diverse range of usage-based insurance schemes for weekly payments.
The paper Pricing weekly motor insurance drivers’ with behavioral and contextual telematics data is available for download at City Research Online. It has been published in Heliyon.
