Professor Rachel Kim
Risk Modelling
Professor Rachel Kim teaches risk modelling and machine learning with an emphasis on model governance and peer review. She is known for her research on transparency and practical evaluation of volatility analytics.
Professor Rachel Kim is recognized for her applied research in risk modelling
and machine learning, focusing on the practical evaluation of volatility
forecasting methods. She has published articles on model risk, transparency, and
the dynamics of peer review in technical analytics. Her work bridges the divide
between theoretical models and their application in operational settings.
As a consultant, Professor Kim advises financial firms and analytics teams on
model governance, technical audit, and best practices for critical review. Her
industry experience spans Canadian banking and international regulatory
projects, where she advocates for robust evidence and ongoing model validation.