Overview
Alongside policy work, authorities continue to drive forward responsible adoption of AI innovation in financial services and institutionalize oversight through more practical measures. These can be broadly grouped into four areas: (1) vehicles for controlled testing and experimentation, (2) adoption of practical toolkits, (3) capacity building activities, (4) public-private dialogue.
What's new
The Monetary Authority of Singapore outlined expanded financial sector workforce measures, including nearly 60 accredited AI programs and training on responsible AI use. Around 1,000 internship and traineeship opportunities from 20 financial institutions will be available over the next year, supported by closer links between education and industry.
The European Supervisory Authorities identified non-EU dependencies, cyber and emerging technology threats, and private credit as key vulnerabilities, while assessing the EU financial system as resilient overall. They called for stronger crisis preparedness, monitoring and stress testing of external and private credit exposures, and early action on risks from artificial intelligence and quantum computing.
The Bank of Italy reported that more than 750 experts from 61 central banks joined 49 technical cooperation activities in the first half of 2026. Topics included artificial intelligence, supervisory technology, operational resilience, the digital euro, payments and market infrastructures.
Deep dive
Controlled testing and experimentation
Supervised testing and experimentation continues to be a core mechanism for supporting responsible AI adoption in financial services, allowing firms to pilot use cases under regulatory engagement while giving authorities practical insight into deployment models and implementation issues. Momentum has remained particularly strong in Asia.
In early March, the HKMA, Securities and Futures Commission (SFC), Insurance Authority and Mandatory Provident Fund Schemes Authority (MPFA) jointly launched GenAI Sandbox++, extending the earlier banking-focused initiative across securities and capital markets, asset and wealth management, insurance, MPF and stored value facilities. The initiative focuses on three high-impact areas: risk management, anti-fraud and customer experience. It also continues to emphasise “AI vs AI” approaches, using AI to help manage AI-related risks.
In Singapore, the Monetary Authority of Singapore in June announced the establishment of the Future of Finance Institute, with an initial focus on AI and tokenisation. The institute will connect financial institutions, technology researchers, solution providers and the FinTech ecosystem, and provide shared resources to support broader adoption. Its planned capabilities include a knowledge hub with validated use cases, deployment playbooks, solution-provider information and a capability development roadmap; an innovation garage for industry-wide collaboration; industry sandboxes for controlled testing of AI-enabled workflows; and implementation toolkits, including an updated AI Risk Management Toolkit with implementation guides for agentic AI.
In the UK, the Financial Conduct Authority continued to develop AI Live Testing, which provides a structured but flexible environment for firms to test AI-driven services in real-world conditions with regulatory support and oversight. The initiative is aimed at firms with mature proof-of-concepts that are ready to test in controlled market environments with a view to deployment, and testing is organised across three phases: discovery, framework validation and AI system testing, including shared evaluation. Eight firms joined the second cohort, including Barclays, Experian, Lloyds Banking Group (Scottish Widows) and UBS, with use cases covering AI-enabled targeted support for investments, credit score insights for consumers, agentic payments, anti-money laundering detection and Know Your Customer.
Practical implementation and oversight toolkits
Practical implementation and oversight toolkits are being used to translate AI governance concepts into more operational resources for firms and supervisors. These initiatives include sector-specific frameworks, common terminology, lifecycle-based oversight tools, implementation handbooks and reference materials that can support more consistent deployment and supervision of AI systems.
In Singapore, the Monetary Authority of Singapore has continued to develop a suite of practical resources for AI implementation in the financial sector. In March, MAS concluded Phase 2 of Project MindForge and published an AI Risk Management Toolkit, developed with 24 banks, insurers, capital markets firms and other industry partners. The toolkit includes an operationalisation handbook and case study supplement covering traditional AI, generative AI and emerging agentic AI. In June, MAS announced that the Future of Finance Institute will include implementation toolkits as one of its planned capabilities, including an updated AI Risk Management Toolkit with implementation guides for agentic AI. In July, MAS and industry participants published the Safeguards for Agentic Finance at Runtime white paper under BuildFin.ai. SAFR builds on MindForge and sets out governance checkpoints for AI agents before execution, including policy-bound execution, real-time validation, auditability and interoperability. Industry participants have applied the framework to agent-assisted payments and treasury operations, wealth management and advisory workflows, and client engagement.
In the United States, the Department of the Treasury in February released two resources for AI use in the financial sector: a shared AI Lexicon and the Financial Services AI Risk Management Framework. The lexicon is intended to establish common definitions across regulatory, technical, legal and business functions, supporting clearer communication and more consistent implementation. The framework adapts the NIST AI Risk Management Framework to financial services and provides practical tools and reference materials for evaluating AI use cases, managing AI across the lifecycle and embedding accountability, transparency and resilience into deployment decisions.
At the global level, IOSCO in May published its Supervisory Toolkit for AI Use in Capital Markets. The toolkit is intended to support securities regulators in supervising and overseeing AI-based systems used by regulated entities. It covers the lifecycle of AI systems across traditional machine learning, generative AI and emerging agentic AI, and sets out three complementary layers: areas for supervisory consideration, tools for supervisory oversight, and indicators for monitoring AI adoption and use.
Capacity building activities
Recently, greater emphasis has been given to formalizing capacity building activities across the financial sector via authority-led programmes. The focus is on the build out of practical AI capabilities, including among students, early-career professionals, as well as seasoned practitioners and executives.
In May, the Monetary Authority of Singapore announced the Young Talent Programme for AI in Finance, developed with the Institute of Banking and Finance, financial institutions, universities and technology partners. The programme will provide students with applied AI and financial-sector skills through industry-relevant projects, mentorship and potential internship or traineeship opportunities involving AI applications.
In January, the Central Bank of Egypt, through FinTech Egypt and the Digital Academy, launched a specialised AI Diploma for Bankers in cooperation with New Giza University, the Egyptian Banking Institute and Injaz Institute. The diploma covers AI fundamentals for banking, core AI techniques and tools, data management and privacy, AI governance and ethics, and AI implementation and operationalisation.
In May, the Central Bank of Uzbekistan and the Ministry of Digital Technologies organised professional development courses on AI for executive staff of regional bank branches. The courses cover the application of AI in banking processes, compliance and governance, process structuring, complex AI processes and working with AI agents, while also giving participating banks a forum to exchange implementation experience.
In January, the National Bank of Georgia, in partnership with Ant Group and the 10x1000 Tech for Inclusion platform, launched AI Career Builder for Fintech Professionals. The programme is aimed at fintech and financial sector professionals and uses a blended learning model combining self-paced modules, self-assessment and live expert sessions on the role of AI in financial innovation and professional development.
Young Talent Programme for AI in Finance
In May, the Monetary Authority of Singapore announced the Young Talent Programme for AI in Finance, developed with the Institute of Banking and Finance, financial institutions, universities and technology partners. The programme will provide students with applied AI and financial-sector skills through industry-relevant projects, mentorship and potential internship or traineeship opportunities involving AI applications.
AI Diploma for Bankers
In January, the Central Bank of Egypt, through FinTech Egypt and the Digital Academy, launched a specialised AI Diploma for Bankers in cooperation with New Giza University, the Egyptian Banking Institute and Injaz Institute. The diploma covers AI fundamentals for banking, core AI techniques and tools, data management and privacy, AI governance and ethics, and AI implementation and operationalisation.
AI Development Courses for Bank Executives
In May, the Central Bank of Uzbekistan and the Ministry of Digital Technologies organised professional development courses on AI for executive staff of regional bank branches. The courses cover the application of AI in banking processes, compliance and governance, process structuring, complex AI processes and working with AI agents, while also giving participating banks a forum to exchange implementation experience.
AI Career Builder
In January, the National Bank of Georgia, in partnership with Ant Group and the 10x1000 Tech for Inclusion platform, launched AI Career Builder for Fintech Professionals. The programme is aimed at fintech and financial sector professionals and uses a blended learning model combining self-paced modules, self-assessment and live expert sessions on the role of AI in financial innovation and professional development.
Public-private dialogue
Public-private dialogue is intended to build a shared understanding of AI use cases, benefits and implementation issues, and to help inform policy direction and the evaluation of supervisory needs. Authorities in several jurisdictions have established project-based or permanent working groups, taskforces, advisory committees and roundtables.
In January, the Commodity Futures Trading Commission launched the Innovation Advisory Committee, renamed from the former Technology Advisory Committee. The committee is intended to provide advice and recommendations to the CFTC on technological innovation in financial services, derivatives and commodity markets, including AI, blockchain and cloud computing.
In March, the U.S. Department of the Treasury’s Office of the Financial Stability Oversight Council and AI Transformation Office held the AI Innovation Series, a set of four public-private roundtables. The discussions covered AI strategy and governance, value generation and efficiency, cybersecurity and risk management, and financial stability and economic security implications.
In April, the Financial Services Agency established a public-private working group to examine cybersecurity measures in the financial sector amid rising AI-related threats. The initiative involves the financial industry, government, the Bank of Japan and other stakeholders, and is intended to develop a common understanding of AI-enabled cyber threats and consider forward-looking responses.
In April, the Financial Services Commission announced plans to establish regional public-private joint committee as part of its upgraded fintech support programs. The committees will bring together financial firms, local government and venture startups to develop regionally tailored fintech service models, including in relation to AI transformation.
Innovation Advisory Committee
In January, the Commodity Futures Trading Commission launched the Innovation Advisory Committee, renamed from the former Technology Advisory Committee. The committee is intended to provide advice and recommendations to the CFTC on technological innovation in financial services, derivatives and commodity markets, including AI, blockchain and cloud computing.
AI Innovation Series
In March, the U.S. Department of the Treasury’s Office of the Financial Stability Oversight Council and AI Transformation Office held the AI Innovation Series, a set of four public-private roundtables. The discussions covered AI strategy and governance, value generation and efficiency, cybersecurity and risk management, and financial stability and economic security implications.
Public-Private Working Group
In April, the Financial Services Agency established a public-private working group to examine cybersecurity measures in the financial sector amid rising AI-related threats. The initiative involves the financial industry, government, the Bank of Japan and other stakeholders, and is intended to develop a common understanding of AI-enabled cyber threats and consider forward-looking responses.
Regional Public-Private Joint Committee
In April, the Financial Services Commission announced plans to establish regional public-private joint committee as part of its upgraded fintech support programs. The committees will bring together financial firms, local government and venture startups to develop regionally tailored fintech service models, including in relation to AI transformation.