黄一凡老师和中国人民大学孟生旺教授等人共同撰写的学术论文“Actuarial intelligence in auto insurance: Claim frequency modeling with driving behavior features and improved boosted trees”于2022年9月被Insurance Mathematics & Economics 接受发表。

论文摘要

Usage-based insurance (UBI) is now a sought-after auto insurance product in the market. By using a widerange of telematics data, insurance companies can better understand the insured’s driving behavior and capture the relationship between insurance loss and the relevant risk factors. This study examines the frequency of UBI claims and combines machine learning algorithms with classic actuarial distributions to establish the predictive model. More specifically, considering the large number of driving behavior features and their complex interactions, we replace generalized linear models with boosted trees, and synchronously update the estimation results of the zero-inflation probability and mean parameter under a zero-inflated Poisson or zero-inflated negative binomial assumption. We further discuss the role of regularization terms and “dropout” in dual-parameter boosted trees, and propose a general framework for insurance claim frequency modeling, which shows high prediction accuracy on both UBI and French motor third-party liability datasets, as well as the interpretability. The potential of extensive driving behavior features has been further verified on a Chinese insurance dataset, and the factors that have a significant impact on vehicle risk are identified and quantified on this basis. In addition, we discuss in detail the key points of applying boosted trees in actuarial science, which also promotes predictive insurance analytics.

作者介绍

黄一凡，对外经济贸易大学统计与精算学系讲师（链接）