教育部人文社科重点研究基地中央财经大学中国精算研究院学术活动

精算论坛第290期讲座

（2026年7月30日）

报告题目：A Portfolio-Anchored Frequency–Severity Behavioral Risk Index for Trip and Driver Assessment Using Telematics Signals

报告人：Andrei Badescu教授，University of Toronto

报告人简介：Andrei Badescu 是多伦多大学（University of Toronto）统计科学系教授（Professor, Department of Statistical Sciences）。他于多伦多大学任教多年，在精算科学、风险管理和金融保险领域具有深厚的学术造诣和广泛的影响力。Badescu 教授目前担任精算学顶级期刊 《Insurance: Mathematics and Economics》 的副主编（Associate Editor），并活跃于国际精算与风险管理学术圈。他的教学涵盖精算科学高级专题、可信度与模拟、保险风险模型等课程。Badescu 教授的研究成果丰硕，已发表学术论文40余篇，研究领域涉及风险与破产理论、矩阵解析方法、随机索赔准备金、相依性建模及预测分析等。

报告摘要：In this presentation, we propose a novel frequency–severity joint trip-level behavioral risk index that combines the frequency of driving patterns with a severity component reflecting how extreme such patterns are relative to a portfolio-level baseline. Severity is quantified through an inverse-probability penalty that increases with the rarity of observed tail extremes, not claim size. For high-frequency telematics data, we construct a multi-scale representation using the Maximal Overlap Discrete Wavelet Transform (MODWT), which preserves localized driving patterns across multiple time scales. To capture severity as tail rarity, we model the portfolio distribution using a Gaussian–Uniform mixture with a layered tail structure, where Gaussian components describe typical driving patterns, and the tail is partitioned into ordered severity layers that reflect increasing extremeness. We develop a likelihood-based estimation procedure that makes inference feasible for this mixture model. The estimated severity layers are then used to construct multi-layer tail counts (MLTC) at the trip level, which are modeled within a Poisson–Gamma framework to yield a closed-form posterior risk index that jointly reflects frequency and severity. This conjugate structure naturally supports sequential updating, enabling the construction of dynamically evolving driver-level risk profiles. Using the UAH-DriveSet controlled dataset, we demonstrate that the proposed index separates instructed behavioral states, such as normal versus aggressive and drowsy, and identifies relatively high-risk trips. The proposed model improves binary classification of normal versus risky trips, with sensitivity analyses showing that its performance is stable.

讲座时间：2026年7月30日（周四），14:00-15:30

报告地点：学院南路校区学术会堂606会议室

邀 请 人：刘子宁

（撰稿：刘子宁；审稿： 王庆焕；编辑： 薛丽娜；审核：马冰）