AI, agents and data science for financial supervision.For those ready to work directly with data and AI tools on real supervisory challenges.
Hands-on from week two, in the GovSpace AI Gym: real supervisory use cases, synthetic data and agentic tools, without touching live systems. No prior coding or data-science experience required.
- Low-income countries$800
- Middle-income countries$1,000
- High-income countries$1,200
- per seat · World Bank classification
5% off per seat for 2–5 seats, 10% for 6–10, 15% for 11 or more. All three courses: a further 10%. Institutional packages: academy@govspace.io.
Technical confidence, grounded in real supervisory use cases.
240 environments, 93 data sources and 40 open code libraries. Design, test and validate supervisory AI applications without risking live systems.
- Practical, hands-on familiarity with data science, AI and agentic systems: advanced analytics, data strategy and machine-learning techniques.
- Proficiency in extracting intelligence from raw data, so strategic decisions rest on evidence.
- A working grasp of data governance and ethics: responsible handling of sensitive supervisory data and compliance with regulatory requirements.
- The ability to communicate data-driven insight to technical and non-technical stakeholders alike.
“Its dynamic and immersive learning approach has equipped me with the skills to inform data-driven decisions in insurance supervision with confidence.”
“If you're serious about innovating with SupTech in your organization, take the program. There is so much to learn from the content, assignments and speakers.”
For supervisors ready to move from strategy to hands-on capability.
Who want to see what AI and data science can do on their own supervisory questions, and judge it for themselves.
In financial authorities, building or buying the analytics that supervision runs on.
Who want the supervisory context, governance and ethics around their technical work.
Ready to turn strategy into working capability. No prior coding experience required.
“Signed up for the program with little knowledge and experience in data science, and ended it with a better appreciation and understanding of the practical use of data science in supervisory technology.”
Six weeks. Three modules. Three hands-on case studies in the AI Gym.
Get oriented and inspired
- Why AI and data science matter in financial supervision
- The value proposition for supervisors and for data scientists
- Think beyond existing roles, toward an interdisciplinary perspective
AI and data science as SupTech: a primer
- De-hype AI: generative AI, agentic AI and traditional machine learning, and when each applies
- Roles, tools and best practice for AI, agents and data science
- The SupTech Taxonomy and GovTech Generations models, and how to use them
- Key applications in SupTech, including agentic architectures and LLM-driven workflows
AI and data science in the wild
- Identify the supervisory value proposition of each AI or data-science solution
- Practice the implementation through interactive notebooks and agentic tools
- Change the parameters of synthetic model versions and see what moves
- Weigh how agentic approaches augment or differ from traditional data science
- Three case studies, one a week, in the live AI Gym. Machine-learning classification, NLP analysis and agentic workflow design, on supervisory datasets.
Deploying AI and data-science products in supervision
- Responsible use of AI and data-science tools
- Ethics in deployment: bias, transparency and accountability
- AI governance frameworks for the public sector
- Team structures for AI and data science in financial supervision, and where to build skills
A brief for an AI application in your own agency.
You propose an AI or data-science application for a real supervisory challenge in your organization, drawing on the tools explored in the course, from machine learning and NLP to agentic workflows, and make the case for how to deploy it.
“Combining data science with real-world application empowered me to build strategic and technology-driven tools that make a real impact. It's not just a course; it's a catalyst for innovation in public service.”
- Machine-learning classification of consumer complaints
- NLP reading of board risk culture from meeting minutes
- Monitoring crypto marketing on social media with AI
- Automated analysis of ESG reports for compliance monitoring
Accessible for every institution.
One list price, set by the World Bank income classification of your country. Volume discounts for teams. Take all three Academy courses for a further 10% off.
- Lower incomeLowest seat price
- Middle incomeMiddle seat price
- Higher incomeHighest seat price
Based on the World Bank income level of your organization's country.
- Low-income countries$800 per seat
- Middle-income countries$1,000 per seat
- High-income countries$1,200 per seat
“This program is one of the few that connects data science and AI directly to real supervisory problems. It's practical, hands-on, and focused on tools we actually use, from PDF extraction to NLP, clustering and market monitoring.”
Taught by practitioners.
Past guest faculty includes
Rweyemamu BarongoHead, AI & Data Innovation Hub · Bank of Tanzania
Iman van LelyveldHead of the Data Science Hub · Dutch Central Bank
Jochen PapenbrockHead, Financial Tech · NVIDIA
Minerva TantocoCEO, City Strategies & Former CTO · City of New York
Vijay Singh ShekhawatChief General Manager, Supervision · Reserve Bank of IndiaBefore this course.
The primer: what AI can do in a public authority, a readiness self-assessment and an AI strategy worksheet.
Strategic fluency, and a facilitated diagnostic of your own agency while you learn.
Start free, or join the next demo.
Your first Pathway, the Readiness Baseline, is free. A demo is a standing call you join alongside other authorities.
One email a month.
What changed on the platform, what the sector is reading, and the next cohort and event dates.
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