报 告 人：Professor Zhuo Jin, Department of Actuarial Studies and Business Analytics, Macquarie University, Sydney, Australia

报告地点：金融工程研究中心 105学术报告厅

报告时间：2023年12月18日 下午16：00—17:00点

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：Zhuo Jin received his B.S. in Mathematics from Huazhong University of Science and Technology in 2005 and his Ph.D. in Mathematics from Wayne State University in 2011 (under George Yin). He is an Associate of the Society of Actuaries (ASA). Currently, he is a professor at the Department of Actuarial Studies and Business Analytics at Macquarie University in Australia. Before moving to Macquarie University in 2022, he was a faculty member at the Centre for Actuarial Studies, Department of Economics, The University of Melbourne for 10 years. His research interests include stochastic optimal control, numerical methods for stochastic systems, stochastic games, dividend optimization, retirement planning, portfolio selection, optimal reinsurance, model ambiguity, machine learning, reinforcement learning, systemic risk, cyber risk, pandemic risk, and climate risk. His publications appear in most of the major actuarial science journals and some prestigious journals on control, system science and OR.