1 week

• Develop an understanding of the context and importance of AI and data science in financial supervision
• Distinguish the value proposition of AI and data science for both financial supervisors and data scientists
• Feel inspired to think beyond the boundaries of existing roles towards an interdisciplinary perspective

1 week

• De-hype AI: distinguish between generative AI, agentic AI, and traditional ML — and when each applies
• Describe AI, agents, and data science roles, tools, and best practices
• Discuss the SupTech Taxonomy and SupTech Generations models and their application
• Identify key applications of AI and data science in suptech, including agentic architectures and LLM-driven workflows

3 weeks

• Work hands-on inside the GovSpace AI Gymnasium — the only dedicated sandbox environment built for financial supervisory use cases
• Identify the supervisory value proposition of each AI or data science solution
• Practice via interactive notebooks and agentic tools the technical details of implementation
• Investigate the implications of changing parameters in synthetic model versions
• Evaluate how AI and agentic approaches augment or differ from traditional data science methods

Three interactive case studies: One week per case study — from ML classification, to NLP analysis, and agentic workflow design. All conducted in the live GovSpace AI Gym with real supervisory datasets. No prior coding experience required.

1 week

• Define the importance of responsible use of AI and data science tools
• Identify ethical considerations when deploying AI — including bias, transparency, and accountability
• Understand AI governance frameworks relevant to the public sector
• Categorise AI and data science management and team structure in financial supervision
• Weigh opportunities for capacity and skills application within institutional contexts

Build a conceptual brief proposing an AI or data science application for a real supervisory challenge within your own organisation — drawing on the tools and techniques explored throughout the course, from machine learning and NLP to agentic workflows and LLM applications. Demonstrate critical thinking and apply new analytical frameworks to make strategic decisions on deployment.