我院副教授、应用统计科学研究中心研究员高光远在《Machine Learning》发表论文。该研究提出了两种数据驱动的神经网络对车联网数据进行自动特征工程，同时提升车险索赔频率的预测模型。实证发现，低速状态下的急减速更容易造成事故，该研究量化了此驾驶行为风险因子，并发现其与传统的精算风险因子互补，如地区、年龄等。

论文题目

  Boosting Poisson regression models with telematics car driving data

作者介绍

  高光远，中国人民大学统计学院副教授、应用统计科学研究中心研究员。主要研究领域包括非寿险准备金评估方法，贝叶斯统计和MCMC，车险定价模型，copula，车联网大数据分析。在绝大部分精算顶尖期刊发表多篇论文，如《ASTIN Bulletin》，《Insurance：Mathematics and Economics》，《Scandinavian Actuarial Journal》等；由Springer出版独著《Bayesian claims reserving methods in non-life insurance with Stan》；参与编著多本教材。主持国家自科青年项目，Society of Actuaries科研项目等；参与国家社科重大项目等。

英文摘要

  With the emergence of telematics car driving data, insurance companies have started to boost classical actuarial regression models for claim frequency prediction with telematics car driving information. In this paper, we propose two data-driven neural network approaches that process telematics car driving data to complement classical actuarial pricing with a driving behavior risk factor from telematics data. Our neural networks simultaneously accommodate feature engineering and regression modeling which allows us to integrate telematics car driving data in a one-step approach into the claim frequency regression models. We conclude from our numerical analysis that both classical actuarial risk factors and telematics car driving data are necessary to receive the best predictive models. This emphasizes that these two sources of information interact and complement each other.

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