Supervising Artificial Intelligence (AI) in Banking Training Workshop

Course overview

The Supervising Artificial Intelligence (AI) in Banking Training Workshop equips central bank officials, banking supervisors, financial regulators and prudential oversight professionals with the knowledge and practical tools required to assess, monitor and supervise AI adoption across regulated financial institutions.

As banks increasingly deploy AI in credit scoring, fraud detection, anti-money laundering, customer service, algorithmic trading, cybersecurity, collections and regulatory compliance, supervisors must understand how these systems affect safety and soundness, consumer protection, operational resilience and financial stability.

Course objectives

• Assess AI adoption across banks and classify AI use cases according to their materiality, risk exposure and potential impact on financial stability.
• Develop supervisory assessment tools, examination procedures, remediation requirements and monitoring plans for responsible AI adoption in banking.
• Identify AI-related threats involving algorithmic discrimination, model failure, cybersecurity, data leakage, fraud, cloud concentration and operational resilience.
• Conduct risk-based supervisory reviews of traditional AI, machine learning, generative AI, AI agents and third-party AI services.
• Evaluate AI governance arrangements, board and senior management accountability, model validation, data governance and human oversight within regulated financial institutions.

Who should attend

Course content

• Module 1: AI in Banking — Supervisory Landscape and Emerging Applications Artificial intelligence in financial services, distinctions between traditional AI, machine learning, deep learning, generative AI and AI agents, banking AI adoption trends, AI a
• Module 2: Regulatory Frameworks and Supervisory Expectations for AI Application of existing prudential supervision principles to AI, governance and risk management expectations for regulated banks, Basel Core Principles and risk-based supervision, model
• Module 3: AI Governance, Board Accountability and Institutional Oversight Board and senior management responsibilities for AI adoption, AI governance committees and oversight structures, defining accountable executives and model owners, institutional AI
• Module 4: Supervisory Risk Assessment and Materiality Classification Identifying inherent AI risks and existing controls, assessing potential impact on depositors and borrowers, materiality classification of AI systems, risk-based prioritisation of super
• Module 5: Model Risk Management, Validation and Independent Review AI model lifecycle governance, model development and acquisition controls, model documentation and intended-use limitations, independent model validation, conceptual soundness and methodo
• Module 6: Data Governance, Quality, Privacy and Confidentiality Data ownership and accountability, lineage and provenance, completeness and accuracy controls, representativeness of training data, data quality monitoring, handling missing and inconsistent
• Module 7: Algorithmic Fairness, Explainability and Consumer Protection Bias in credit scoring and lending decisions, discriminatory outcomes and proxy variables, fairness assessment across relevant customer groups, explainability of automated decisions,
• Module 8: Generative AI, Large Language Models and AI Agents in Banking Large language models and foundation models, generative AI applications in customer service and internal operations, AI-assisted document review and regulatory reporting, retrieval-a
• Module 9: Cybersecurity, Fraud and Adversarial AI Risks AI-enabled fraud and social engineering, synthetic identities and deepfake-related threats, AI-assisted phishing and impersonation, adversarial attacks against machine learning systems, data poisoni
• Module 10: Credit Risk, Financial Crime and High-Impact Banking Decisions AI in credit underwriting and loan pricing, machine learning in default prediction, AI-driven credit limit management, collections and early-warning systems, fraud detection and tr
• Module 11: Third-Party AI, Cloud Outsourcing and Concentration Risk AI model and platform procurement, cloud-based AI services, external foundation model providers, outsourcing and subcontracting arrangements, due diligence and vendor risk classification
• Module 12: AI in Operational Risk, Capital, Liquidity and Financial Stability AI-related operational risk events and control failures, dependence on AI in critical business services, model-driven market correlations and herding behaviour, common vendor a
• Module 13: Supervisory Technology (SupTech) and AI-Assisted Bank Supervision AI-assisted analysis of regulatory returns, automated extraction and review of supervisory documents, anomaly detection in bank reporting, off-site monitoring and early-warning
• Module 14: On-Site Examinations, Supervisory Findings and Remediation Planning an AI-focused supervisory examination, reviewing institutional AI inventories and governance policies, selecting high-materiality models for review, conducting interviews with
• Module 15: Capstone — Developing an AI Supervisory Framework for Banks Mapping AI adoption across a sample banking sector, developing an institutional AI risk classification framework, designing an AI supervisory questionnaire, preparing an AI risk-sco

General notes

• The workshop examines supervisory expectations for AI governance, board accountability, model risk management, data quality, algorithmic bias, explainability, third-party technology dependencies, generative AI, cybersecurity and incident reporting. It also covers how regulators can use supervisory technology (SupTech), AI-assisted analytics and structured supervisory assessments to identify emerging risks and improve oversight.
• Participants will develop practical supervisory instruments, including an AI supervisory assessment framework, institutional AI risk-scoring matrix, examination checklist, model validation review template and supervisory action plan. The programme is designed for central banks and financial regulators seeking to strengthen risk-based supervision while supporting responsible innovation in the banking sector.
• Practical Exercises and Case Studies AI supervisory risk assessment: Classify a portfolio of banking AI use cases by materiality, risk and supervisory priority. Credit model review: Assess a hypothetical AI lending model for validation quality, explainability, data bias and customer impact. Generative AI examination: Review a bank's proposed internal AI assistant for confidentiality, hallucination, access control and human oversight. Third-party risk assessment: Evaluate a bank's dependence on an external AI model and cloud service provider. Financial crime case study: Examine AI-assisted transaction monitoring for false positives, missed alerts and model drift. Supervisory incident simulation: Respond to a scenario involving AI-driven customer harm, confidential data leakage or widespread model failure. Capstone assignment: Produce a supervisory assessment report, risk matrix and corrective-action plan for a hypothetical regulated bank.
• Expected Practical Outputs By the end of the workshop, participants will have developed or adapted the following supervisory instruments: AI supervisory framework, banking-sector AI inventory template, AI materiality classification matrix, institution-level AI risk assessment scorecard, AI governance examination checklist, board and senior management accountability assessment, model validation review template, data governance and privacy checklist, algorithmic fairness assessment checklist, generative AI supervisory review template, AI third-party and cloud outsourcing risk assessment, AI cybersecurity and adversarial risk checklist, AI incident reporting template, supervisory examination work programme, AI risk heat map, supervisory findings and remediation tracker, AI risk monitoring dashboard specification, AI-related financial stability scenario, supervisory reporting requirements outline, AI supervisory implementation roadmap.
• The Supervising AI in Banking Training Workshop prepares central bank officials, banking supervisors and financial regulators to assess artificial intelligence adoption across regulated financial institutions. The programme covers AI governance, model risk management, algorithmic fairness, generative AI oversight, cybersecurity, third-party technology risk, financial stability and AI-enabled supervisory technology. Through practical case studies and supervisory assessment exercises, participants learn to identify emerging AI risks, evaluate institutional controls and develop risk-based supervisory frameworks for responsible AI adoption in the banking sector.

Frequently asked questions

Schedule & fees