报告时间：2023年12月25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：Markov chain approximation-based deep learning in actuarial science
报告摘要：We will introduce a series of deep learning approaches and its application in insurance decision making problems, where decision makers are subject to the randomness of the financial ruin time to terminate the control processes. Markov chain approximation-based iterative deep learning algorithms are developed to study this type of infinite-horizon optimal control problems. The optimal controls are approximated as deep neural networks. The framework of Markov chain approximation plays a key role in building the iterative equations and initialization of the algorithm. Optimal parameters of neural networks are then obtained iteratively.
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。
