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The explosion of generative AI models and developments in agentic AI are driving a new wave of digital transformation. Regulators across the globe are striving to keep up, particularly in the highly regulated financial services sector. In the fourth edition of this report we partner with IBM, whose research demonstrates how AI-first institutions are already outperforming peers.

We highlight both the opportunities and challenges facing businesses deploying AI in core financial markets, offering a broad overview of the complex and fast-changing regulatory landscape, along with practical guidance for managing risk.

Key themes

The AI revolution in finance – opportunities and challenges

AI adoption is accelerating in financial services, a data-rich industry with a long history of machine learning. With advanced deployments of AI increasing, and significant process mapping already in place, the industry is primed to lead in generative AI, notably AI agents. But, given the highly regulated nature of the sector where consumer protection and market stability are regulatory priorities, firms must understand and manage the associated legal risks.

The global legal landscape for AI

The challenge for lawyers advising business is both to understand how existing legal and regulatory frameworks apply to AI and to keep up with new AI-specific law and regulation as it develops at an international, regional and national level.

AI and financial services regulation

As important as it is to keep one eye on future developments, when implementing AI systems today, financial services firms must do so within the constraints of the existing financial services regulatory framework.

Reconciling AI with global data protection laws

The interaction between AI and data protection legislation is complex and still not fully resolved. In the EU, regulators are considering fundamental issues such as when personal data taken from the internet can be used to train an AI. They also expect those using AI to apply high governance standards, including completing detailed impact assessments.

Regulating AI through competition law – key issues to consider

Antitrust regulators are generally focused on the impact of frontier technologies and developing digital markets on competition. How businesses use emerging technologies, especially AI, is emerging as a key theme for antitrust regulators across the globe. Regulators are not only considering whether competition law is fit for purpose with respect to the impact of AI – for example, with respect to algorithmic collusion, hub and spoke arrangements, tacit collusion and broader harms – but are also balancing these risks with a desire to fuel financial growth and technological innovation.

Practical guidance on managing legal risk in AI

Financial services organisations need to take a holistic, forward-looking approach to anticipating the impact of AI technology on their business. In practical terms, firms need to have a clear understanding of what they want to achieve in deploying any AI tech, and how it will work to achieve that goal, together with a clear plan for identifying and managing and associated technological, reputational and legal risks.

United Kingdom

Singapore

United Kingdom

Light-touch, industry-led: The UK government previously announced that it intends to adopt a light-touch and industry-led approach, meaning that there won’t be specific legislation like the EU AI Act. However, in July 2024 government proposed a set of binding measures on AI and its intention to establish appropriate AI legislation.

Singapore

Mostly self-regulated: The general regulatory approach is to foster AI innovation through the responsible use of AI. The financial regulator has issued AI-friendly guidelines and best practice for regulated firms, while the data regulator has also issued guidance for the use of personal data in AI. In May 2024, the Singapore government published a comprehensive Model AI Governance Framework for GenAI.

Additional resources

Updated AI Toolkit - Ethical, safe, lawful: A toolkit for artificial intelligence projects

Our updated AI Toolkit, crafted by our expert technology, privacy, intellectual property, litigation, employment, competition, ESG and financial regulatory teams, is intended as a quick-reference guide for in-house counsel on all things AI.

This Toolkit starts with a technical primer and provides an overview of key AI compliance, contracting and contentious topics across the EU/UK, Asia and the US.

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Operational Resilience

UK regulators have prescribed how financial institutions and market infrastructure must build their resilience to business disruption.

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EU Digital Package Handbook

The EU is currently pushing through a transformational digital regulation package. However, that package is made up of a number of different laws, such as the Data Governance Act, the AI Act, NISD2, the Digital Services Act and ten other related instruments.
Keeping track of all these new laws is a challenge. Our EU Digital Regulation Handbook provides a short, accessible summary of the status of each law, together with an assessment of comparable developments in the UK. The handbook is available here.

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AI in financial services: Applying operational resilience rules to AI

When overseeing firms’ adoption of AI, regulators will leverage the tools within their operational resilience frameworks, including the EU’s Digital Operational Resilience Act and the corresponding rules in the UK.

During our last webinar of our series on AI in financial services in which we examined:

The intersection of DORA and the EU AI Act
The UK’s approach to operational resilience
The impact of critical third party regimes

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Tech Legal Outlook 2025 Mid-Year Update

We explore the reality of implementing AI, the shifting legal landscape, AI in the Middle East and the role of AI in the energy transition, payments and online safety.

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IBM - The impact of AI

AI has captured the media zeitgeist in recent years, especially since the release of OpenAI’s ChatGPT in 2022. Countless stories probe the technology’s potential future, exploring the advancements of AI and the changes it might bring about. But what has been the real-world impact so far?

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IBM - From AI projects to profits

The initial euphoria surrounding generative AI has given way to a more nuanced, realistic understanding of its potential and challenges.

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IBM - The technology behind InstructLab, a low-cost way to customize LLMs

IBM and Red Hat’s new open-source project is designed to lower the cost of fine-tuning large language models by allowing people to collaboratively add new knowledge and skills to any model.

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