Supervising AI Adoption, Outsourcing and Technology Risk in Financial Institutions Training Workshop

Course overview

The Supervising AI Adoption, Outsourcing and Technology Risk in Financial Institutions Training Workshop equips central bank supervisors, financial regulators, prudential risk specialists and technology-risk professionals with the knowledge and practical frameworks required to assess how financial institutions adopt artificial intelligence, outsource critical functions and manage technology-related risks.

As banks, payment service providers, insurers and other regulated institutions increasingly rely on AI systems, cloud platforms, external technology vendors, third-party data providers and managed technology services, supervisors must be able to evaluate the associated operational, prudential, cybersecurity, data protection, model and concentration risks. The workshop examines the supervisory implications of machine learning, generative AI, AI agents, automated decision-making, cloud outsourcing, software-as-a-service platforms, external AI models and interconnected technology supply chains.

Course objectives

• Assess AI adoption strategies, governance arrangements and risk management frameworks across banks and other regulated financial institutions.
• Develop risk-based supervisory recommendations, remediation requirements, incident response expectations and ongoing monitoring frameworks for AI adoption and technology outsourcing.
• Conduct technology-risk examinations, vendor due diligence reviews, AI governance assessments and operational resilience evaluations using practical supervisory tools.
• Identify and assess model risk, data governance weaknesses, algorithmic bias, cybersecurity threats, operational vulnerabilities and concentration risks associated with AI and outsourced technology.
• Evaluate outsourcing arrangements, cloud services, AI vendors, technology contracts and third-party dependencies using risk-based supervisory approaches.

Who should attend

Course content

• Module 1. Foundations of AI Adoption and Technology Risk Supervision Evolution of AI in financial services, traditional machine learning and generative AI applications, AI agents and automated decision-making, technology-enabled banking business models,
• Module 2. Regulatory Frameworks and Supervisory Expectations for AI International approaches to AI regulation in financial services, governance and accountability expectations, board and senior management responsibilities, risk-based regulatory oversight
• Module 3. AI Governance, Accountability and Institutional Control Frameworks Board oversight of AI adoption, executive accountability, AI governance committees, AI inventories and use-case classification, risk appetite and tolerance, approval processes f
• Module 4. Supervising AI Models, Algorithms and Automated Decisions AI model lifecycle governance, model development and procurement, training data quality, model validation and independent review, model performance monitoring, explainability and interpr
• Module 5. Generative AI, Large Language Models and AI Agents in Financial Institutions Generative AI applications in banking, retrieval-augmented generation, enterprise AI assistants, document processing, automated customer support, AI-assisted complianc
• Module 6. Enterprise Outsourcing and Third-Party Risk Management Outsourcing governance, third-party risk management frameworks, identification of outsourced activities, classification of critical or important functions, materiality assessment, service-p
• Module 7. Cloud Computing, SaaS and Technology Infrastructure Risk Public, private and hybrid cloud models, infrastructure-as-a-service, platform-as-a-service and software-as-a-service, cloud migration risk, shared responsibility models, multi-cloud and
• Module 8. AI Vendor Risk, Technology Procurement and Contractual Safeguards AI vendor selection, technical and financial due diligence, proprietary versus open-source AI, external model and API dependencies, vendor security assessments, software supply-c
• Module 9. Cybersecurity, Data Protection and AI-Specific Threats AI-related cyber threats, data poisoning, adversarial manipulation, prompt injection, model extraction, unauthorised data disclosure, identity and access management, privileged access, secr
• Module 10. Operational Resilience and Critical Technology Dependencies Identification of critical financial services, mapping of people, processes, technology and external dependencies, maximum tolerable disruption, business impact analysis, service avai
• Module 11. Concentration Risk and Systemic Technology Dependencies Concentration in cloud service providers, common AI foundation models, dominant technology vendors, shared data and software platforms, common cybersecurity providers, interconnected outs
• Module 12. Supervisory Examination of AI and Outsourced Technology Preparation of risk-based examination plans, review of AI inventories and governance policies, interviews with board members and technology executives, examination of vendor due diligence
• Module 13. AI Risk Monitoring, Incident Management and Supervisory Reporting Key risk indicators for AI and outsourcing, vendor performance monitoring, AI model drift indicators, service availability metrics, incident severity classification, regulatory
• Module 14. Enterprise Technology Risk Tools, AI Assurance and Governance Automation Enterprise governance, risk and compliance platforms, third-party risk management systems, AI system inventories, automated vendor assessments, regulatory obligation mapp
• Module 15. Integrated Supervisory Capstone: AI Adoption and Technology Outsourcing Review Assessment of a simulated financial institution introducing an AI-based credit decisioning platform hosted by an external cloud provider, identification of critical

General notes

• Participants learn to assess AI governance frameworks, review outsourcing and third-party risk management arrangements, evaluate vendor due diligence, examine critical technology dependencies, scrutinise contractual safeguards and assess institutions' ability to maintain essential financial services during technology disruptions. Particular attention is given to service-provider concentration, cross-border data processing, fourth-party dependencies, cloud exit strategies, AI model validation, algorithmic bias, explainability, cybersecurity and operational resilience.
• The programme combines supervisory frameworks, practical risk assessment exercises, simulated regulatory examinations, AI use-case reviews, third-party due diligence and technology incident scenarios. Participants develop practical tools for assessing AI adoption and technology outsourcing, prioritising supervisory interventions, evaluating remediation plans and strengthening risk-based oversight of regulated financial institutions.
• Practical Exercises and Case Studies Classify AI use cases by business criticality, risk level and potential impact on customers and financial stability. Review an AI governance policy and identify weaknesses in accountability, independent validation and human oversight. Assess an outsourced AI credit scoring solution against model risk, data quality, explainability and consumer protection requirements. Complete a third-party due diligence assessment for a cloud-hosted banking application. Review a technology outsourcing contract for audit rights, regulatory access, incident notification, data ownership and exit provisions. Map fourth-party dependencies and evaluate concentration exposure to a common cloud provider or foundation model. Assess a generative AI deployment for prompt injection, sensitive-data leakage, hallucinations and excessive permissions. Evaluate a cloud service outage scenario and determine its implications for critical banking operations. Review an institution's business continuity plan and test the feasibility of migrating to an alternative service provider. Prepare a risk-based supervisory examination plan for a bank with extensive AI and technology outsourcing arrangements. Develop a technology-risk dashboard covering AI governance, vendor exposure, incidents, service availability and remediation status. Prepare a supervisory findings report with risk ratings, corrective actions, timelines and escalation recommendations.
• Practical Outputs and Deliverables AI adoption supervisory assessment framework, AI use-case inventory and risk classification template, AI governance and accountability assessment checklist, AI model risk and validation review framework, generative AI risk assessment checklist, third-party and outsourcing risk assessment methodology, critical-function materiality assessment matrix, technology vendor due diligence questionnaire, cloud outsourcing risk assessment template, AI and technology outsourcing contract review checklist, third-party and fourth-party dependency register, cloud and AI provider concentration risk assessment, cybersecurity and AI threat assessment checklist, operational resilience and business continuity assessment framework, technology incident and regulatory notification assessment template, risk-based supervisory examination plan, AI and technology risk dashboard specification, supervisory findings and remediation tracking matrix, AI adoption and outsourcing supervisory report, integrated capstone supervisory assessment with prioritised corrective actions.
• AI Governance for Financial Regulators, Banking Technology Risk Management, Third-Party Risk Management Training, Cloud Outsourcing Risk for Banks, AI Risk Management in Financial Services, Financial Sector Operational Resilience, Supervising AI in Banking, Technology Risk Supervision Workshop, AI Vendor Risk Assessment, Central Bank Technology Risk Training.
• Recommended Reference Resources Financial Stability Board (FSB), The Financial Stability Implications of Artificial Intelligence (2024). Financial Stability Board (FSB), Monitoring Adoption of Artificial Intelligence and Related Vulnerabilities in the Financial Sector (2025). Basel Committee on Banking Supervision, Principles for Operational Resilience and guidance on third-party risk management. European Banking Authority (EBA), Guidelines on Outsourcing Arrangements. National Institute of Standards and Technology (NIST), AI Risk Management Framework. International Organization for Standardization, ISO/IEC 42001, Artificial Intelligence Management Systems. International Monetary Fund (IMF), AI Projects in Financial Supervisory Authorities (2025).

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