Claims frequency modeling using telematics car driving data |
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Authors: | Guangyuan Gao Shengwang Meng Mario V Wüthrich |
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Institution: | 1. Center for Applied Statistics and School of Statistics, Renmin University of China, Beijing, China;2. Department of Mathematics, ETH Zurich, Zurich, Switzerland |
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Abstract: | We investigate the predictive power of covariates extracted from telematics car driving data using the speed-acceleration heatmaps of Gao, G. & Wüthrich, M. V. (2017). Feature extraction from telematics car driving heatmaps. SSRN ID: 3070069]. These telematics covariates include K-means classification, 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. |
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Keywords: | Telematics data K-means algorithm principal components analysis bottleneck neural network autoencoder generalized additive model v-a heatmap pattern recognition Kullback-Leibler divergence claims frequency modeling car insurance pricing |
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