FinTech Training · Powered by MindForge
AI Risk Managementand Governancefor Risk andControl Functions
Operationalising AI Governance in Financial Institutions
- Format
- Hybrid
- Duration
- 11.5 hours
- Programme Start Date
- TBC
Programme overview
Grounded in the frameworks and practical guidance developed under MAS Project MindForge, this programme goes beyond theoretical concepts to equip participants with the skills to operationalise AI governance and risk management within financial institutions.
Through real-world case studies, interactive discussions and application-based exercises, participants will learn to assess AI-related risks, evaluate governance controls and apply practical risk management approaches across generative and agentic AI use cases.
Who is this programme for?
This programme is ideal for:
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Compliance, legal, and governance professionals responsible for applying AI policies and ensuring ethical AI use
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Risk and internal audit professionals in the second and third lines of defence overseeing GenAI and Agentic AI adoption
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This programme is also relevant for:
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- Senior managers and team leads accountable for compliant and risk-aware AI adoption within their functions
Industry context & MindForge guidance
MAS AI Risk Management Guidelines (Oct 2026) establish clear accountability for boards, senior management, and AI governance functions — including oversight of AI systems procured from vendors or cloud providers.
Source: MAS Guidelines on AI Risk Management, 2026; MAS Project MindForge Operationalisation Handbook, 2026
AI sits at the intersection of data science, business units, and IT — creating ownership ambiguity that traditional governance models were not built to resolve. MindForge’s response: formalised accountability at board, senior-management, and AI-governance-committee level.
Source: MAS Project MindForge Toolkit, 2026
Programme learning outcomes
- Understand AI risks and ethical frameworks in financial services.
- Strengthen organisational and use-case risk management.
- Apply governance controls across the AI lifecycle.
View detailed learning outcomes
Illustrate the fundamentals of AI risks and Ethical AI frameworks in the context of financial services governance
Adopt organisational-level risk management frameworks to incorporate AI-specific risks and controls
Examine the role of custodians in identifying, escalating, and managing AI-specific risks within their areas of accountability
Apply case-level risk management framework to address AI-specific risks across the end-to-end AI lifecycle
Programme fees & IBF funding
Course Code: TGS-2026066594
Eligible individuals
Singapore Citizens aged 40 years and above
SGD 491.60²Including 9% GSTInternational Participants
SGD 1,553.25Including 9% GST¹50% capped at S$3,000 per participant per course
²70% capped at S$3,000 per participant per course
All fees stated above are net fees payable after subsidies and include 9% GST.
This course has been accredited under the IBF Standards: Future-Enabled Skills and is eligible for funding under the IBF Standards Training Scheme (IBF-STS), subject to all eligibility criteria being met.
Participants are advised to assess the suitability of the course and its relevance to his/her business activities or job roles. The IBF-STS is available to eligible entities and individuals based on the prevalent funding eligibility, quantum and caps. IBF-STS provides up to 70% course fee subsidy support for direct training costs subject to a cap of S$3,000 per candidate per course subject to all eligibility criteria being met.
Find out more on www.ibf.org.sg
About IBF Certification
This course addresses the following Technical Skills and Competencies (TSCs) and proficiency level:
Basic – Generative AI Regulatory, Legal, and Risk Mat Compliance (SFW-BAS-0010-0.0)
Participants are encouraged to access the IBF MySkills Portfolio (https://www.ibf.org.sg/home/for-individuals/resource-tools/myskills-portfolio) to track their training progress and skills acquisition against the Skills Framework for Financial Services. You can apply for IBF Certification after fulfilling the required number of Technical Skills and Competencies (TSCs) for the selected job role.
Find out more about IBF certification and the application process on https://www.ibf.org.sg/home/for-individuals/ibf-certification/why-be-ibf-certified
Programme details
E-learning (Asynchronous)
Introduction to AI Governance and Risk Management
Explore the evolving AI landscape, including traditional, generative, and agentic AI, and understand the key principles of responsible AI governance. Learn how ethical AI frameworks, regulatory expectations, and lifecycle oversight support effective AI risk management in financial institutions.
Ensuring Effective AI-related Policies, Procedures and Standards
Understand how financial institutions develop and implement AI governance policies, procedures, and standards. Discover how to integrate AI governance into existing organisational frameworks, establish clear responsibilities, and promote consistent practices across the organisation.
Enhancing Organisational-Level Risk Management
Learn how to identify, assess, and manage AI-related risks at an organisational level. Explore AI risk classification, risk appetite, key risk indicators, and monitoring approaches to strengthen existing enterprise risk management frameworks.
Uplifting Practices for Managing Third-Party AI Risks
Understand the risks associated with third-party AI solutions and service providers. Learn how to evaluate vendor disclosures, strengthen procurement and contractual safeguards, and establish ongoing oversight to manage third-party AI risks effectively.
Enhancing Use Case-Level Risk Management
Explore practical approaches to assessing and managing AI risks throughout the lifecycle of individual use cases. Learn how to determine risk materiality, evaluate controls, implement appropriate safeguards, and conduct pre-deployment and ongoing reviews.
Ensuring AI Inventory Capabilities
Understand the importance of maintaining a comprehensive AI inventory to support governance, transparency, and accountability. Learn how to document AI use cases, track associated risks and controls, and establish clear ownership for ongoing inventory management.
Physical Workshop (Synchronous)
Recap of Asynchronous Lesson
Reinforce key concepts from the asynchronous learning modules through interactive discussions and practical scenarios. Explore how AI governance principles and risk management frameworks apply to real-world generative and agentic AI use cases across the financial sector.
Recent Trend(s) and What They Mean for Risk and Compliance
Explore the latest developments in generative and agentic AI and their implications for financial institutions. Understand how increasing AI autonomy introduces new operational, regulatory, and compliance challenges across banking, insurance, and capital markets.
Case Study One — Gen AI Risk Materiality and Guardrails
Apply AI risk assessment frameworks to practical financial services use cases. Through group exercises, learn to identify and classify AI risks, assess risk materiality, and recommend appropriate safeguards aligned with existing enterprise risk management frameworks.
Discussion — Gen AI Controls, Data Issues and the Second-Line Challenge Questions
Examine practical AI controls, data governance challenges, and monitoring metrics. Learn how to interpret AI testing results, evaluate control effectiveness, and formulate critical risk and compliance questions to support responsible AI deployment.
Case Study Two — Third-Party Risk Management: Vendor LLM, Assessed via the AI Card
Gain hands-on experience evaluating third-party AI solutions using the MindForge AI Card framework. Learn to assess vendor transparency, identify potential risks, and recommend contractual safeguards, due diligence measures, and ongoing monitoring controls.
Data Governance for Gen AI
Explore the data governance challenges associated with generative AI, including data privacy, accuracy, confidentiality, and regulatory compliance. Through practical exercises, develop data protection controls and monitoring plans that integrate with existing organisational governance frameworks.
Agentic AI — Workflow Autonomy, Human Oversight, Kill Switches and KRIs
Understand the unique risks introduced by autonomous AI agents operating within financial workflows. Learn to design human oversight mechanisms, establish operational boundaries, implement emergency intervention controls, and define key risk indicators to monitor agentic AI activities.
Roundtable: Dealing with Regulatory Change in Gen AI / Agentic AI
Engage in discussions on emerging AI regulations, supervisory expectations, and their implications for financial institutions. Explore how organisations can adapt their governance frameworks, risk appetite, and compliance strategies to respond effectively to evolving regulatory requirements.
* The curriculum is subject to changes to reflect the evolving advancements in AI and the needs of financial services industry.
Programme Director
Over 30 years of experience in financial services and digital innovation across Asia and the Middle East.
Read full biography
Prof. Tek Yew Chia is a leading voice in financial services and digital innovation, with over 30 years of experience spanning strategy, technology, and regulation across Asia and the Middle East. He is Adjunct Professor and Advisor at the NUS Asian Institute of Digital Finance, and formerly Vice Chairman of Oliver Wyman Singapore and Head of Financial Services Advisory in KPMG Singapore.
Prof. Chia is concurrently a Senior Advisor in Boston Consulting Group (BCG) and also serves as an advisor to the ASEAN Bankers Association (ABA) and the Global Finance and Technology Network (GFTN – a spin-off from the Monetary Authority of Singapore). A sought-after speaker, he brings sharp insights on the future of finance at the intersection of policy, technology, and business.
Register your interest
Contact our team for more information about the programme.