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Claims?Frequency?Modeling? Using?Telematics?Car?Driving?Data
日期: 2018-07-20

Bio:

Dr. Guangyuan Gao, a lecturer of Renmin University of China, graduated from the Australian National University School of Business, Institute of Finance, Actuarial and Applied Statistics, Ph.D. in Statistics; from 2010 to 2011, Master of Applied Statistics and Master of Actuarial Science from the Australian National University; Graduated from Tongji University. Research interests: non-life insurance reserve assessment model, vehicle network big data analysis, insurance reserve stochastic model, copulas and risk metrics.


Abstract:

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 classification, principal components, and bottleneck activations from a bottleneck neural network. 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. For this reason we recommend the use of these telematics covariates for car insurance pricing.



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