FinTech and policy leaders must now leverage AI to safeguard financial systems against shocks and crises. This 5-day intensive training explores how AI-driven early-warning systems, stress testing, real-time market surveillance, and crisis analytics can enhance macroprudential frameworks. Participants will engage with cutting-edge tools, case studies, and data governance practices from leading international institutions to strengthen financial resilience in an AI-augmented world.
- AI-powered early warning systems for systemic risks
- Machine learning for stress-testing and synthetic scenario generation
- Real-time AI-driven 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 agent-based and dynamic AI
- Integration within international bodies (IMF, FSB, ESRB) and regulatory standards
- Design AI-enabled early warning dashboards for detecting systemic stress
- Build ML-based stress-testing frameworks with synthetic scenario layers
- Apply GNNs to analyze financial contagion and network fragility
- Use real-time data feeds and NLP models to flag market anomalies
- Implement agent-based 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 AI-informed financial stability policies
- Central bank macroprudential, financial stability, and surveillance teams - International financial institutions (IMF, BIS, FSB, ESRB) - National supervisors and financial regulators - Risk analytics, stress-testing, data science teams in banking sector - Financial infrastructure providers and fintech innovators
- Hands-on labs using Python, PyTorch/Graph ML, spaCy - Case studies from IMF GFSR, BIS AI governance and ECB/BoE practices - Simulation workshops using real-world market and network data - Expert seminars on governance, ethics, systemic oversight - Peer review panels on model design and explainability - Capstone project: deploy an AI-based crisis monitoring prototype
- 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 early-warning dashboard using Python & spaCy
- Discussion: balancing detection efficiency with false positives
- 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
- Graph neural nets for link-level risk assessment
- Agent-based simulation of shocks with AI
- Case: AI-enabled contagion mapping for emerging markets
- Lab: implement a GNN to detect weak links in financial networks
- Group exercise: simulate contagion via agent-based modeling
- NLP for event-based risk classification
- AI lexical tracking: news, social, regulatory signal mining
- Lab: build real-time anomaly detection pipeline
- Discussion: Herding, correlated AI strategies and systemic risk
- Governance roles of central bank surveys & AI consortia
- 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
Group & Corporate Discounts: Available for companies enrolling multiple participants to help maximize ROI. Individual Discounts: Offered to self-sponsored participants who pay in full and upfront. Registration Process: Corporate nominations must go through the client’s HR or Training department. Self-nominations must be prepaid via the “payment by self” option. Confirmation: All registrations are subject to DIXONTECH’s approval and seat availability. Refunds: Provided in case of course cancellation or no seat availability. Tax Responsibility: Clients are responsible for any local taxes in their country.