Advances in AI are enabling multiple intelligent agents to collaborate, make decisions and execute complex tasks with increasing autonomy. In financial systems, these capabilities could support new approaches to economic simulation, policy analysis, supervisory monitoring and increasingly autonomous operations.

While offering significant opportunities to enhance decision-making and system efficiency, they also introduce new questions around governance, validation, accountability, systemic behaviour and financial stability. This roundtable will critically assess these emerging capabilities, associate risks and examine the implications for central banking.

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.

Financial institutions are moving well beyond experimentation with AI, deploying it across credit decisioning, fraud detection, and everyday employee workflows at genuine scale. But how they get there differs sharply: some are building their own foundation models on proprietary data to avoid depending on external providers with their most sensitive information; others adopt commercial AI tools but wrap them in anonymization and strict governance before sensitive data reaches them; and some capital allocators are treating AI infrastructure itself — compute, data centres, foundation models — as a strategic asset worth direct ownership.

Regulation is catching up fast and unevenly, with major regimes now treating data location and third-party AI access as a risk to be supervised, not just a technology choice. Given the different AI strategies already in motion, what would it actually take to make them interoperable, auditable, and defensible across jurisdictions?