Abstract: Telematics car driving data describes drivers' driving characteristics. This paper studies the driving characteristics at different speeds and their predictive power for claims frequency modeling. We first extract covariates from telematics car driving data using K-medoids clustering and principal components analysis. These telematics covariates are then used as explanatory variables for claims frequency modeling, in which we analyze their predictive power. Moreover, we use these telematics covariates to challenge the classical covariates usually used in practice.

Bio: Dr Guangyuan Gao is a lecturer in School of Statistics in Renmin University. He has a Bachelor degree with Tongji University and a Ph.D with the Australian National University. Dr Guangyuan Gao has published a wide range of academic journal articles related to telematics data analysis in Scandinavian Actuarial Journal, Insurance: Mathematics and Economics, European Actuarial Journal and Risks. He is familiar with contemporaneous literature in telematics data analysis. He also has published several papers of claims reserving in ASTIN Bulletin and North American Actuarial Journal, a book of Bayesian analysis with Springer.