Time:
Nov.23(Monday), 9:00-10:00
Venue:
Zoom Conference ID: 691 9464 0501
Speaker:
Zhuo Jin, Senior Lecturer of Division of Business and Economics at The University of Melbourne
Summary:
This work studies a deep learning approach to find optimal insurance strategies for insurance companies. Due to the randomness of the financial ruin time to terminate the control processes, a Markov chain approximation-based iterative deep learning algorithm is 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. Convergence of the algorithm is studied. Satisfactory computation efficiency and accuracy are achieved as presented in numerical examples.
Brief introduction of the speaker:
Dr Zhuo Jin obtained a BS in Applied Mathematics from Huazhong University of Science and Technology, China in 2005. He joined the Centre for Actuarial Studies at the University of Melbourne as a lecturer after he completed his PhD in Mathematics from Wayne State University, US, in 2011. He was appointed as a senior lecturer in 2016 and he is an associate of Society of Actuaries. Dr Zhuo Jin's research fields include Stochastic Optimal Control, Actuarial Science and Mathematical Finance. He has published more than 40 papers on international journals, such as SIAM Journal on Control and Optimization, Automatica, European Journal of Operational Research, Insurance Mathematics and Economics.
