AI for Macro-Financial Stability & Crisis Monitoring

FinTech and policy leaders must now leverage AI to safeguard financial systems against shocks and crises. This 5day intensive training explores how AIdriven earlywarning systems, stress testing, realtime market surveillance, and crisis analytics can enhance macroprudential frameworks. Participants will engage with cuttingedge tools, case studies, and data governance practices from leading international institutions to strengthen financial resilience in an AIaugmented world.

TRAINING TOPICS

• AIpowered early warning systems for systemic risks
• Machine learning for stresstesting and synthetic scenario generation
• Realtime AIdriven market monitoring and anomaly detection
• Graph neural networks for financial network stability analysis
• NLP for event risk classification and sentiment tracking
• AI risk governance, explainability, and macroprudential oversight
• Crisis propagation modelling with agentbased and dynamic AI
• Integration within international bodies (IMF, FSB, ESRB) and regulatory standards

BY THE END OF THIS TRAINING COURSE, DELEGATES WILL BE ABLE TO:

• Design AIenabled early warning dashboards for detecting systemic stress
• Build MLbased stresstesting frameworks with synthetic scenario layers
• Apply GNNs to analyze financial contagion and network fragility
• Use realtime data feeds and NLP models to flag market anomalies
• Implement agentbased and dynamic AI models to simulate crisis propagation
• Evaluate AI model risks, explainability, and compliance with FSB standards
• Integrate AI insights into existing macroprudential tools and governance structures
• Collaborate with international bodies on AIinformed financial stability policies

TRAINING IS TAILORED TO

• Central bank macroprudential, financial stability, and surveillance teams
• International financial institutions (IMF, BIS, FSB, ESRB)
• National supervisors and financial regulators
• Risk analytics, stresstesting, data science teams in banking sector
• Financial infrastructure providers and fintech innovators

TRAINING METHODOLOGY

• Handson labs using Python, PyTorch/Graph ML, spaCy
• Case studies from IMF GFSR, BIS AI governance and ECB/BoE practices
• Simulation workshops using realworld market and network data
• Expert seminars on governance, ethics, systemic oversight
• Peer review panels on model design and explainability
• Capstone project: deploy an AIbased crisis monitoring prototype

DAY ONE:

AIENABLED EARLY WARNING & MONITORING

• Overview of systemic risk frameworks (IMF GFSR perspectives)
• AI applications in financial surveillance (ECB, BIS use cases)
• Live data ingestion: market, regulatory, alternative sources
• Lab: build an earlywarning dashboard using Python & spaCy
• Discussion: balancing detection efficiency with false positives

DAY TWO:

MACHINE LEARNING & STRESSTESTING FRAMEWORKS

• Generating stress scenarios via synthetic data
• Ensemble ML for risk estimation (FRM, early warning)
• Governance: explainable ML in regulation
• Lab: deploy ensemble models for stress test use case
• Peer review: model assumptions & transparency

DAY THREE:

NETWORK ANALYSIS & CRISIS PROPAGATION

• Graph neural nets for linklevel risk assessment
• Agentbased simulation of shocks with AI
• Case: AIenabled contagion mapping for emerging markets
• Lab: implement a GNN to detect weak links in financial networks
• Group exercise: simulate contagion via agentbased modeling

DAY FOUR:

REALTIME MARKET SURVEILLANCE & NLP

• NLP for eventbased risk classification
• AI lexical tracking: news, social, regulatory signal mining
• Lab: build realtime anomaly detection pipeline
• Discussion: Herding, correlated AI strategies and systemic risk
• Governance roles of central bank surveys & AI consortia

DAY FIVE:

GOVERNANCE, INTERNATIONAL COORDINATION & CAPSTONE

• AI governance frameworks (FSB, BIS, ESRB standards)
• Macroprudential integration: data sharing and global collaboration
• Lab: capstone project – deploy full crisis monitoring prototype
• Final presentations & critique panels
• Roadmap session: embedding AI tools into institutional strategies