How can AI agents interpret regulation reliably when people already struggle to keep pace with its volume and complexity? Trusted agentic AI requires authoritative regulatory information organised so that people and machines can understand and use it consistently.

This is fundamentally a human-and-machine model. Machines can analyse regulation systematically and at scale, connecting findings to evidence. People contribute legal and institutional context, exercise judgment and remain accountable for consequential decisions. Neither should function in isolation: uncertainty and disagreement must be visible and escalated for human review.

This workshop will explore the regulatory data foundations needed to support this partnership. Participants will consider how shared ways of classifying rules and defining regulatory concepts could enable agents to retrieve the correct provisions, validate interpretations and cite authoritative sources—while reducing duplication for public authorities and global banks.

The discussion will examine different approaches to organising and governing regulatory information, including whether shared standards can enable interoperable systems without requiring a single global classification framework or central database. It will also consider how to preserve jurisdictional authority and institutional control.

Participants will gain a clearer understanding of how to combine machine scale and consistency with human context, judgment and accountability.

While AI is creating new opportunities across financial services, its effectiveness ultimately depends on access to high-quality and trusted data. Building on emerging open data sharing initiatives across the financial sector, this roundtable will explore how financial institutions can collaborate to unlock greater value from data while preserving privacy, security, and trust. Bringing together industry leaders, technology providers, AI practitioners, and policymakers, the discussion will examine practical approaches to data sharing, privacy-enhancing technologies, common data standards, and trusted data ecosystems that can accelerate AI innovation, enable the development of more capable and reliable financial AI models, strengthen risk management, and support the next generation of AI-enabled financial services.

Globally, regulators are each moving ahead with their own AI governance tracks, ranging from voluntary frameworks to draft legislation and sector-specific rules. This growing regulatory fragmentation, if not managed well, could raise the compliance burden for regional fintechs and banks, and ultimately undermine broader goals of achieving integrated digital markets, with multinational financial institutions having to account for different risk-classification, testing, and disclosure regimes in their AI use-cases.

Using Southeast Asia as a case, this roundtable takes stock of how ASEAN member states regulate and supervise AI in financial services, and where closer alignment is within reach. Participants, including ASEAN central banks, banking associations, and regional financial institutions, will discuss the most pressing near-term opportunities to leverage and challenges to address.

Roundtable outputs will feed into efforts to build a long-term implementation roadmap to facilitate greater alignment across AI governance in ASEAN, with lessons relevant to any region working to bring AI rules closer together.