Supervising and Regulating AI in the Financial Sector

• Foundations of AI and Generative AI: evolution from ML to LLMs, transformer models, fine-tuning, and RAG
• AI development lifecycle and MLOps: data, pipelines, monitoring, and cost/ICT considerations.
• AI in finance: key market trends and sectorspecific use cases
• Governance and risk management: bias, adversarial threats, systemic risks, and governance frameworks
• Risk-based supervision and auditing: explainability, fairness metrics, stress testing, and bias detection
• Technical documentation: supervisory checklists on risk, data, transparency, oversight, and cybersecurity
• Cybersecurity and AI: privacy threats, defence mechanisms, and post-quantum readiness
• Regulatory landscape: EU AI Act and interactions with major financial regulations
• Practical exercises on AI risk assessment and auditing
• AI agents

• Understand the evolution of AI technologies, including LLMs and generative AI, and their relevance to financial services
• Analyse the AI model development lifecycle and identify key supervisory touchpoints
• Recognise and assess financial-sector AI use cases and their associated risks
• Apply governance and risk management principles to evaluate AI systems, including bias, robustness, and model drift
• Use core supervisory tools such as explainability techniques, fairness metrics, and stress-testing approaches
• Review and interpret technical documentation required for AI systems under supervisory frameworks
• Identify cybersecurity vulnerabilities and appropriate mitigation strategies for AI models
• Navigate the EU AI Act and understand how it interacts with existing financial regulations
• Conduct structured assessments of AI systems through practical auditing and risk-analysis exercises

The ideal participant should have either successfully completed the online course on “AI in the financial sector” offered within the EU-SDFA or have an
initial, solid understanding of AI and ML models, and their use in the financial system.

Essential: Understanding of core technical elements of AI and ML models, such as unsupervised learning, reinforcement learning, deep learning etc.

Recommended: Adequate experience in financial regulation and supervision in consumer protection, financial stability, market monitoring and conduct supervision