AI for Early Detection of Financial Crises and Emerging Banking Vulnerabilities Training Workshop

Course overview

The AI for Early Detection of Financial Crises and Emerging Banking Vulnerabilities Training Workshop equips central bank economists, financial regulators and financial stability analysts with practical methods for strengthening early-warning systems. The programme covers banking vulnerability indicators, machine learning, anomaly detection, credit deterioration, liquidity risk, financial networks, macroprudential surveillance and stress testing. Participants learn to integrate AI-generated risk signals with established econometric models, supervisory intelligence and expert judgement to improve financial stability monitoring and support timely policy decisions.

Financial crises often develop through interconnected pressures involving deteriorating credit quality, excessive leverage, liquidity shortages, asset price bubbles, foreign exchange exposures, capital outflows, funding concentration and weaknesses in institutional risk management. Traditional monitoring frameworks remain essential, but they may struggle to detect complex interactions across large, rapidly changing and heterogeneous financial datasets.

Course objectives

• Identify leading indicators of banking vulnerabilities and financial crises using macroeconomic, banking, market and high-frequency data.
• Design an interpretable early-warning framework with risk thresholds, monitoring dashboards, escalation procedures and policy response options.
• Integrate AI-generated risk signals with macroprudential indicators, stress testing, supervisory intelligence and expert economic judgement.
• Develop and evaluate models for banking distress, credit deterioration, liquidity pressure, market instability and systemic risk.
• Apply machine learning, anomaly detection and AI-assisted analytical methods to strengthen financial stability monitoring and early-warning systems.

Who should attend

Course content

• Module 1: Financial Crises, Banking Vulnerabilities and Early-Warning Frameworks Historical patterns of banking and financial crises, origins of systemic banking distress, financial cycles and vulnerability accumulation, credit booms and excessive levera
• Module 2: Financial Stability Data Architecture and High-Frequency Monitoring Macroeconomic and financial data sources, regulatory returns and supervisory datasets, bank balance sheets and income statements, credit registry and non-performing loan data,
• Module 3: Banking Vulnerability Indicators and Macrofinancial Surveillance Capital adequacy and capital buffers, leverage ratios and balance-sheet expansion, non-performing loans and provisioning trends, loan-to-deposit ratios, liquidity coverage and fun
• Module 4: Econometric Early-Warning Models and Time-Series Analysis Statistical foundations of financial early-warning systems, binary response models for banking distress, logistic and probit regression, panel data models, vector autoregression and macr
• Module 5: Machine Learning for Banking Distress Detection Supervised learning for financial vulnerability classification, defining target variables and prediction horizons, decision trees and random forests, gradient boosting and XGBoost, support vector
• Module 6: AI-Based Anomaly Detection and Emerging Risk Signals Anomaly detection in bank balance sheets, identifying unusual deposit movements, detecting abnormal credit growth, monitoring unexpected liquidity fluctuations, unusual interbank transactions
• Module 7: Credit Risk Deterioration and Banking Sector Vulnerability Forecasting Early signs of credit portfolio deterioration, sectoral credit concentration, borrower default indicators, arrears and delinquency transitions, probability of default estima
• Module 8: Liquidity Risk, Funding Pressures and Deposit Stability Liquidity risk in banking institutions, deposit concentration and depositor behaviour, funding maturity mismatches, wholesale funding dependence, interbank market pressures, liquidity cove
• Module 9: Financial Networks, Contagion and Systemic Risk Analytics Interbank lending and borrowing networks, financial institution interconnectedness, counterparty exposure mapping, network centrality and systemic importance, contagion through direct fi
• Module 10: Market Intelligence, Alternative Data and High-Frequency Risk Signals Financial market indicators and banking stress, government bond yield spreads, sovereign risk premiums, exchange rate volatility, equity market movements, bank stock perform
• Module 11: Macroprudential Surveillance and System-Wide Vulnerability Assessment Objectives of macroprudential supervision, cyclical and structural systemic risks, credit-to-GDP gap analysis, excessive credit growth and leverage, countercyclical capital
• Module 12: Financial Stress Testing, Scenario Analysis and Crisis Simulation Purpose and design of financial stress tests, baseline and adverse macroeconomic scenarios, recession and output shock assumptions, inflation and interest rate shocks, foreign e
• Module 13: Explainable AI, Model Validation and Early-Warning System Governance Explainable AI in financial stability analysis, model documentation and governance, feature importance and local explanations, SHAP and other interpretability approaches, ide
• Module 14: Financial Stability Dashboards, Supervisory Escalation and Policy Communication Designing executive financial stability dashboards, combining leading indicators and model-generated signals, bank-level and system-level risk views, vulnerability
• Module 15: Capstone — Developing an AI-Assisted Financial Crisis Early-Warning Framework Defining a banking vulnerability scenario, identifying relevant financial stability indicators, constructing a structured monitoring dataset, selecting an early-wa
• Practical Exercises and Case Studies Banking vulnerability assessment: Analyse a sample banking dataset to identify institutions showing deteriorating asset quality, rising leverage or weakening liquidity. Early-warning indicator design: Develop a set o

General notes

• Artificial intelligence, machine learning, anomaly detection, network analytics and high-frequency data monitoring offer additional methods for identifying unusual financial patterns, assessing vulnerability build-ups and strengthening early-warning systems. When integrated with established econometric models, macroprudential indicators, supervisory information and expert judgement, these techniques can help authorities improve the timeliness and depth of financial stability assessments.
• This practical workshop examines AI-assisted financial crisis monitoring, banking vulnerability indicators, systemic risk analysis, credit deterioration forecasting, liquidity stress detection, interconnectedness analysis and macrofinancial scenario modelling. Participants will learn to evaluate alternative early-warning models, develop interpretable risk indicators, assess model performance and translate analytical signals into actionable supervisory and macroprudential responses.
• The programme emphasises that AI models generate risk signals rather than certainty about future crises. Effective early-warning systems require reliable data, sound economic reasoning, transparent validation, appropriate uncertainty measures and clear escalation procedures.
• Through practical case studies and a capstone exercise, participants will develop an AI-assisted financial vulnerability monitoring framework, an early-warning indicator dashboard specification and a policy response framework suitable for a central bank or financial stability authority.

Frequently asked questions

Schedule & fees