Boosting Poisson regression models with telematics car driving data

With the emergence of telematics car driving data, insurance companies have started to boost classical actuarial regression models for claim frequency prediction with telematics car driving information. In this paper, we propose two data-driven neural network approaches that process telematics car driving data to complement classical actuarial pricing with a driving behavior risk factor from telematics data. Our neural networks simultaneously accommodate feature engineering and regression modeling which allows us to integrate telematics car driving data in a one-step approach into the claim frequency regression models. We conclude from our numerical analysis that both classical actuarial risk factors and telematics car driving data are necessary to receive the best predictive models. This emphasizes that these two sources of information interact and complement each other.

Automated summary

This paper introduces two data-driven neural network approaches for utilizing telematics car driving data to enhance classical actuarial regression models for claim frequency prediction. The study concludes that both classical actuarial risk factors and telematics car driving data are necessary to achieve the best predictive models, emphasizing the interaction and complementarity of these two sources of information.