Can the finance sector oversee AI innovation while maintaining its rapid progress?
Disclaimer: The opinions expressed and arguments employed herein are solely those of the authors and do not necessarily reflect the official views of the OECD, the GPAI or their member countries.
Artificial intelligence (AI) is no longer an emerging feature of financial systems. From credit scoring and fraud detection to financial advice and customer support, AI is reshaping how finance operates. Yet, as AI innovation accelerates, so too does a critical policy question: How can regulators keep pace with AI to ensure positive outcomes for consumers and markets? And how can they identify and assess emerging risks to market integrity and stability?
By focusing on supervisory practice – how existing financial services rules are interpreted, implemented and enforced – policymakers can create an environment where innovation in financial markets thrives without compromising trust, stability and the pace of innovation.
Strong regulatory foundations, complex supervisory realities
Under the broadly technology-neutral principle that guides financial regulation in OECD economies, existing requirements apply regardless of the technology used to deliver a financial service or product. Financial supervision serves as the practical enforcement mechanism for financial regulation, ensuring that policies translate into effective oversight and resilient financial markets.
Practical implementation of AI policies in finance may face challenges due to the intrinsic characteristics of AI innovation, especially advanced AI such as agentic AI systems, the growing volume and speed of transactions and the opacity and complexity of some advanced models that challenge human oversight, as well as their potentially autonomous nature. Limited data on AI adoption by financial services firms makes it difficult to evaluate its use and may hinder monitoring of associated vulnerabilities and their impact on markets and consumers more generally.
The OECD report Supervision of Artificial Intelligence in Finance offers a timely analysis of supervisory approaches and practices that support policy objectives while promoting safe and responsible AI adoption in finance, ensuring that innovation and oversight evolve together.
Supervisory challenges and barriers to wider AI adoption in finance
In some jurisdictions, evaluating bodies and firms seeking compliance face similar difficulties, which could directly impede the wider AI uptake in financial services. Many of the challenges are predictable and include model risk management and validation, testing the outcomes of complex AI systems and limited AI explainability. Challenges also include the practical assessment of the robustness and fairness of model outputs, and data governance and management – many elements highlighted in the OECD AI Principles.
For example, many find it challenging to articulate how the “human in the loop” concept should work in practice. Others struggle to determine appropriate fairness thresholds (including what industry benchmarks should look like) and assess whether they are adequate.
Effective oversight of AI in finance is more about better supervision than new regulation
While existing requirements still apply and supervised entities are expected to adapt their risk-management frameworks to AI-specific matters, some jurisdictions could benefit from guidance and clarification on how these existing regulatory requirements apply to advanced AI models.
In some instances, guidance might clarify ambiguities in interpretation and implementation, for example, in model risk management frameworks, especially regarding issues such as model validation, testing and monitoring, as well as regulatory requirements, such as governance.
Rather than imposing rigid, overly prescriptive requirements that could hinder AI adoption, providing interpretative guidance and practical clarifications on applying existing risk-management and governance frameworks in AI contexts may be preferable, where necessary.
From static compliance to dynamic understanding
Closer engagement between supervisors and industry stakeholders, beyond standard supervisory activities, could foster mutual understanding. Such engagement can yield significant benefits for supervised entities by helping to clarify ambiguity, while also improving authorities’ understanding of the challenges they face in their supervisory efforts.
One way financial supervisors can engage with the industry is through AI-specific testing, which can provide confidence and clarity, encouraging innovation while protecting markets, their participants and stability.
Initiatives such as the UK’s Financial Conduct Authority (FCA) AI Live Testing programme could help promote constructive dialogue between firms developing or using AI and regulatory agencies, encouraging mutual understanding and learning.
Novel approaches to AI supervision: the FCA’s AI Live Testing
The FCA’s AI Live Testing programme allows financial services firms to work directly with its regulatory and technical teams when deploying AI systems in controlled live market environments. The programme focuses on how an AI model interacts with its environment. It is not a simulated lab environment, but rather aims to understand the behaviour of deployed AI systems, taking relevant rules and regulations into account. The objective is to support safe, responsible AI adoption without creating new AI‑specific rules, while giving firms regulatory confidence and helping the FCA understand how AI affects UK markets and consumers.
How AI Live Testing works: From discovery to live testing
The AI Live Testing programme comprises two phases: one for discovery and one for live testing. The programme is designed to be proportionate, with a strong emphasis on in‑person engagement, structured workshops and iterative feedback.
The discovery phase is intended to build a clear understanding of the AI system used by a given firm, including its testing and monitoring approach, governance arrangements, controls and key areas of potential risk. Through a series of continued engagement (including in-person workshops) and voluntary evidence‑gathering, the AI Live Testing team assesses:
- AI system architecture and design
- Model development and performance
- Data integrity and pipeline management
- AI robustness and reliability
- Explainability and transparency
- Monitoring, logging and version control
- Security and technical resilience
The live testing phase includes a practical assessment of the firm’s testing and monitoring approaches. Building on the discovery phase, it uses structured workshops and feedback sessions to explore:
- How to test the AI use case most effectively
- What evidence is required to demonstrate that an AI system is safe and responsible, including delivering the right outcomes for consumers and markets
- How testing can demonstrate alignment with FCA regulations and requirements
It should be noted that the FCA does not test the AI system on the firm’s behalf. Firms are responsible for testing, pilots and evidence generation, including demonstrating that a given AI system is safe and responsible. AI Live Testing’s role is to analyse the approach taken by firms, review evidence and identify areas where risk management, governance or the interpretation of policies and test results may be unclear or require adjustment to secure the best outcomes for consumers and markets. This ensures that responsibility for the technology, including risk identification and mitigation, remains with the firm.
Observed benefits of FCA’s AI Live Testing
The aim of AI Live Testing is to help firms design, deploy and monitor safe and responsible AI, not to provide regulatory approval, audit or sign-off. To achieve this, it uses the following approaches:
- AI flaws are always understood in the context of the firm’s AI use case that is within the scope of AI Live Testing. This avoids an esoteric or overly technical view of harms, keeping the focus on real-world consumer impact.
- The programme is designed to encourage two-way learning between the FCA and participating firms through feedback loops at all key stages of discovery and testing. It is intended to be exploratory rather than supervisory, focused on insights rather than regulatory judgements, approvals, AI audits or endorsements.
- No “hands-on” testing: The FCA does not test the system for the firm. It analyses firms’ testing, monitoring and piloting approaches and provides feedback on potential gaps in the firm-level testing regime.
AI Live Testing helps build an evidence-based understanding of what works in safe and responsible AI deployment, especially where ambiguities exist, and what may need to change in testing, governance, risk management and continuous improvement
Issues brought to the surface by advances in AI innovation
To date, AI Live Testing has identified two critical challenges:
- The first issue is ensuring that AI governance keeps pace with increased complexity as a result of the pace of technological innovation combined with the pace of adoption and deployment of these more advanced AI systems. In practice, this means that effective policies and frameworks are key in designing and deploying safe and responsible AI. This also requires confidence in testing, risk mitigation strategies and the ability of individual firms to monitor.
- The second issue concerns agentic AI systems. They have multiple possible execution paths, but all relevant eventualities cannot be tested prior to deployment. This presents a fundamental challenge in pre-deployment testing because, by definition, this process is insufficient. Often, the response is to fall back on human control. However, this is a limited means of effectively verifying all the potential actions of an AI model.
These developments raise further questions: What do they mean for the overall effectiveness of AI system governance, including the role of human oversight? Where can regulatory requirements on governance, accountability, and systems and controls make a positive difference? And how can regulatory requirements adapt over time to stay relevant and deliver the right outcomes for consumers and markets?
Flexible, agile and adaptive AI supervision
Maintaining a flexible, agile, and adaptive stance towards supervision is one way financial supervisors can keep pace with technological advances, such as agentic AI. Novel methodologies and techniques could enrich the supervisory toolkit and help to ensure beneficial innovation, stability and trust. They also provide an opportunity for supervisors to ask the right questions of firms in a timely manner, based on insights from these toolkits. Public-private dialogue and proactive engagement between supervisors and regulated entities should support the adoption of new supervisory methods and tools. This collaboration will serve as the foundation for deploying safe and responsible AI in financial markets. At the same time, it enables institutional capacity building, which is key to more effective monitoring of AI activity.
By remaining open to enriching and adapting frameworks to reflect the realities and specific characteristics of AI innovation, financial supervisors can foster responsible AI innovation in finance while mitigating associated risks. This shift will not happen overnight and will require investment, experimentation, co-operation with industry and, above all, a willingness to rethink and enrich long-standing institutional practices.
