KSY026: AI in Action: Transforming Capital Markets and Wealth Management Strategies

 

Learning Outcomes

1. Identify common AI tools and data sources used in capital markets and wealth management.

2. Explain how AI technologies such as machine learning, natural language processing, and data analytics support business and investment decisions.

3. Apply AI insights to support day-to-day decision-making in business and investment contexts.

4. Differentiate between traditional decision-making approaches and AI-augmented strategies.

5. Evaluate the benefits, limitations, and risks of AI adoption, including data quality, model bias, and governance considerations.

Course Contents

  • Foundations of Artificial Intelligence in Finance
  • Introduction to AI: key concepts and terminology • Overview of AI technologies (machine learning, NLP, automation, analytics) • Evolution of AI in capital markets and wealth management • Benefits and limitations of AI adoption • Case example: Early AI use in financial services
  • AI Technologies and Business Applications
  • AI applications in capital markets: trading, research, surveillance, and risk management • AI applications in wealth management: client profiling, robo-advisory, portfolio optimisation • Data requirements and data governance • Discussion: Selecting the right AI tools for business needs
  • AI-Enhanced Decision-Making
  • Role of AI in improving speed, accuracy, and consistency of decisions • Human judgement vs AI-driven insights • Scenario analysis and predictive analytics • Managing over-reliance on AI • Case study: AI-supported investment decision-making
  • Risks, Ethics, and Governance of AI
  • Model risk, data bias, and explainability • Ethical considerations in AI-driven decisions • Regulatory and compliance expectations • Governance frameworks for responsible AI use • Case Study: Managing AI risks in practice
  • Implementing AI for Strategic Advantage
  • Integrating AI into business and investment strategies • Change management and skills required for AI adoption • Measuring performance and value creation • Developing an AI adoption roadmap