Jun Yuan

Dr. Jun Yuan is a seasoned professional and a recognized thought leader in financial risk management, with expertise encompassing climate risk, financial innovation analytics, and financial system regulatory policy reform.

He currently serves as Managing Director, Group Risk Management at Royal Bank of Canada (RBC), where he leads risk analytics teams in Canada and the US to ensure the best industry practices in risk management. His specialties include market risk, counterparty credit risk, Comprehensive Capital Analysis and Review stress testing, machine learning, climate risk analytics, and other areas. As a pioneer in the application of machine learning to financial innovation, he has employed unsupervised learning for detecting anomalies in market data and natural language processing for sentiment analysis and rating predictions.

In the field of climate risk, Yuan is instrumental in advancing RBC’s climate analytics capabilities. He co-leads the RBC climate scenario analysis working group, ensuring compliance with regulations by the Office of the Superintendent of Financial Institutions (OSFI) in Canada. His recent work includes identifying a transmission channel from climate transition risks, such as policy changes, to micro-prudential market risk via secondary trading in financial markets. This discovery has been recognized by the Canadian Bankers Association and is under consideration by OSFI for integration into their Standardized Climate Scenario Exercise methodology. Additionally, he has successfully defended RBC’s climate risk management strategies before the Bank of England's Prudential Regulation Authority. In policy advocacy, Yuan has played a significant role in shaping international regulatory requirements under Basel IV, notably initiating and developing a sensitivity-based method for market risk, which has been integrated into the Basel Accord's Fundamental Review of the Trading Book rules.

Parallel to his career in the financial sector, Yuan is a dedicated contributor to applied research, teaching, and community service. He has collaborated and published with Bank of Canada partners and University of Toronto faculty on machine learning research initiatives, including a study on narrative monetary policy uncertainty using natural language processing, and research on deep hedging for trading using reinforcement learning. In 2023, Yuan was awarded a Rotman School of Management Dobson Business & Climate Grant for his climate analytics research.

Yuan has lectured in the Rotman School of Management MBA, Master of Financial Risk Management (MFRM), and executive training programs for more than ten years, and serves on the advisory boards for the Rotman School’s FinHub and MFRM program. He is also regularly sought as a speaker at international finance conferences and adviser to leaders from government regulatory bodies and global financial institutions.

Yuan holds PhD (University of Toronto) and Master’s (Queen’s University) degrees in Electrical and Computer Engineering.

Updated January 2024