Financial Risk Management with Machine Learning (Synchronous E-Learning)
About This Course
Amidst the challenges of a rapidly evolving financial landscape, effective financial risk management has become essential for organisations seeking to navigate uncertainties and safeguard their assets against credit and market risks.
This course aims to equip participants with a comprehensive understanding of financial risk management, focusing on credit and market risks, while incorporating machine learning techniques to enhance the modeling and management of these risks. By combining foundational risk management principles with hands-on applications using Python, the course provides a balance between academic theory and practical implementation.
What You'll Learn
• Understand the fundamentals of financial instruments, risk-return tradeoffs, and portfolio theory
• Measure and manage credit and market risks using traditional models and advanced machine learning techniques
• Apply machine learning models, such as regression, clustering, and neural networks, to improve risk prediction, credit scoring, and portfolio optimisation
• Implement Value at Risk (VaR), Expected Shortfall (ES), and other risk measures using Python
• Use Python for portfolio optimisation, risk modeling, and stress testing, incorporating modern risk-adjusted performance measures
• Leverage machine learning for risk-aware decision-making
Entry Requirements
• Participants are encouraged to have a fundamental understanding of Python or R, and those without programming experience should consider taking introductory online courses.
• Basic knowledge of financial concepts, including familiarity with financial instruments, credit, and market risk, is recommended.
• Fundamental understanding of statistical concepts like mean, variance, and correlation is essential for implementing risk models and machine learning algorithms.