报告时间：2023年12月26日 18:30开始
报 告 人： 金卓（澳大利亚麦考瑞大学商学院 教授）
会议地点： 9号楼 218 报告厅
报告题目：Emerging risk management and data techniques in insurance
报告摘要：For cyber risk management, a cluster-based method is developed to investigate the risk of cyber-attacks in the continental United States. The proposed analysis considers geographical information on cyber incidents for clustering. By clustering state-based observations, the frequency and severity of cyber losses demonstrate a simplified structure: independent structure between inter-arrival time and size of cyber breaches. The independence between frequency and severity is significant at the state level instead of the national level. It is shown that the cluster-based models have a better fitting and are more robust than the aggregate model, where all incidents are considered together. To detect fraud insurance claims, we propose a new variable importance methodology incorporated with two prominent unsupervised deep learning models, namely, the autoencoder and the variational autoencoder. Each model's dynamics are discussed to inform the reader on how models can be adapted for fraud detection and how results can be perceived appropriately with a greater emphasis placed on qualitative evaluation.
报告人简介：金卓，澳大利亚麦考瑞大学商学院教授。研究方向包括最优控制论在精算中的应用，数理金融，金融科技，机器学习与金融交叉。在Insurance Mathematics and Economics, European Journal of Operational Research, Journal of Risk and Insurance, SIAM Journal on Control and Optimization, Automatica等顶级期刊发表论文60余篇。他还是包括Society of Actuaries在内的多个重要学术组织的成员。
