The Masterclass consists of five sessions delivered across two days. Each is designed to address critical areas of private investing with clear learning goals and fresh perspectives:
- The Fundamentals of AI in Investing
- Topics: Core AI concepts (machine learning, NLP, generative AI); key data types; myths vs. realities.
- Learning goals: Build foundational understanding; set realistic expectations; analyze success and failure case studies.
- AI-Driven Investment Decision-Making
- Topics: Machine learning in quantitative investing; AI-enhanced allocation; predictive analytics; equity and fixed income use cases.
- Learning goals: Understand model-driven insights; improve forecasts; recognize risks of overfitting or instability.
- Organizational Transformation and Human-AI Collaboration
- Topics: Designing an AI-ready investment organization; governance; new skills and roles.
- Learning goals: Embed AI responsibly into workflows; manage cultural and talent challenges; explore case studies of successful integration.
- Ethical and Governance Dilemmas in AI for Investing
- Topics: Bias in models; explainability; regulatory frameworks; fiduciary duty.
- Learning goals: Recognize ethical dilemmas; build ethical AI adoption frameworks; understand emerging regulatory expectations.
- AI Frontiers: Alternative Data, Automation, and Future Outlook
- Topics: Alternative data sources; large language models; back- and middle-office automation; future trends in asset management.
- Learning goals: Leverage alternative data and generative AI; apply automation to reduce costs; anticipate long-term AI shifts in investment firms.