Claims Frequency Modeling Using Telematics Car Driving Data
Scandinavian Actuarial Journal, Forthcoming
23 Pages Posted: 23 Jan 2018 Last revised: 23 Sep 2018
Date Written: January 15, 2018
We investigate the predictive power of covariates extracted from telematics car driving data using the speed-acceleration heatmaps of Gao and Wüthrich (2017). These telematics covariates include K-means classication, principal components, and bottleneck activations from a bottleneck neural network. In the conducted case study it turns out that the first principal component and the bottleneck activations give a better out-of-sample prediction for claims frequencies than other traditional pricing factors such as driver's age. Based on these numerical examples we recommend the use of these telematics covariates for car insurance pricing.
Keywords: Telematics Data, K-Means Algorithm, Principal Components Analysis, Bottleneck Neural Network, Generalized Additive Model, v-a Heatmap, Pattern Recognition, Kullback-Leibler Divergence, Claims Frequency Modeling, Car Insurance Pricing
JEL Classification: G22
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