Causal-GNN and Survival Analysis in Finance and Economics
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Course Description
This course explores the integration of causal inference, graph neural networks (GNNs), and survival analysis to model economic and financial dynamics. Participants will learn how to capture time-to-event processes (firm exit, institutional collapse, credit default) by embedding graph structures into hazard models. The course combines econometric rigor with advanced AI, focusing on interpretability, fairness, and policy relevance. (Syllabus)
AI for Risk Management and Fraud Detection in Financial Ecosystems
Course Description
This course provides a rigorous introduction to the use of artificial intelligence, graph neural networks, and causal inference in managing risk and detecting fraud in financial systems. Participants will learn how relational structures in financial transactions can be leveraged to detect fraud rings, reduce systemic vulnerabilities, and design fair risk-scoring systems. Emphasis will be placed on regulatory compliance, explainability, and policy implications. (Syllabus)
Managing AI at Scale: Metrics, Governance, and Business Impact
This course examines how Data and Artificial Intelligence create, sustain, and scale value within organizations, from a strategic and managerial perspective. Rather than focusing on algorithms or tools, it develops an integrated framework that connects model performance, data quality, governance, cost structures, and measurable business impact into a coherent decision-making logic.
Students learn how production metrics, model risk, data drift, fairness, monetization, and organizational adoption interact across the full AI lifecycle, shaping both short-term returns and long-term institutional capabilities. The course emphasizes AI as a strategic asset, highlighting trade-offs between innovation, control, resilience, and value realization.
Through applied analytical frameworks and executive-level diagnostics, the course equips participants to evaluate AI initiatives, govern analytics portfolios, and translate technical performance into economic and organizational outcomes. Demonstrations rely on synthetic but internally consistent data, ensuring realism while preserving confidentiality.
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