[Publication Time] 2021-03-01

[Lead Author] 高光远

[Corresponding Author] Wuethrich, Mario, V

[Journal] MACHINE LEARNING

[Abstract]

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.

[Keywords]

Densely connected feed-forward neural network; Convolutional neural network; Combined actuarial neural network; Claims frequency modeling; Telematics car driving data; Poisson regression; Generalized linear model; Regression tree; Telematics heatmap