Regulations should address the effects, behaviours, outcomes and consequences of tech, say panellists
[SINGAPORE] As artificial intelligence agents increasingly move beyond analysing information to also executing transactions, this gives rise to the question of managing risk, especially in the areas of liquidity and collateral, said Chia Der Jiun, managing director of the Monetary Authority of Singapore (MAS) on Friday (Oct 9).
“We are entering a world where AI agents may increasingly act on behalf of individuals and institutions, not only to analyse information, but also initiate and execute financial transactions and other consequential actions,” he noted.
Chia was speaking at a conference organised by Insead at its Asia Campus. The conference, titled: “The Future of Digital Finance: Infrastructure, AI and Regulation”, brought together academics, industry leaders and policymakers.
With the foundational capabilities of blockchain and digital assets already proven, the focus has shifted to commercial viability as well as trust.
“The question is no longer simply whether these technologies work,” he added. “The more important question is what is needed to operate safely, commercially and at scale. Innovation must be founded on trust and stability if it is to scale.”
As the use of AI agents increasingly intersects with tokenised assets and digital money, Chia warned of “significant implications” on the movement of capital. Because customer agents can autonomously optimise and execute trades, this could result in money moving around faster “for smaller differences in benefits and incentives”, he said, noting that financial institutions will thus have to “sharpen their value proposition”. He pointed out that this acceleration poses implications for risk management, particularly concerning liquidity and collateral.
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“While agents create significant opportunities for efficiency, accessibility and innovation in financial services, they also have to be deployed safely,” he said. “This then requires controls around authority, permissions, identity, accountability and governance.”
Chia added that financial institutions “must be able to ensure” that AI agents act within the scope of a user’s instructions, institutional policies and risk limits, while complying with regulatory requirements.
Earlier this year, MAS and industry participants published the Safeguards for Agentic Finance at Runtime (SAFR) white paper. Additionally, through the Future of Finance Institute, MAS has released an open-source reference implementation of SAFR to support industry experimentation.
“The technology is still developing rapidly, and many questions remain,” said Chia. “A public-private partnership approach… will remain essential to form coalitions of expertise around these key questions and build up our body of standards and good practices.”
Developing regulations
During a separate panel discussion on AI agents and blockchain rails, moderated by Jason Davis, associate professor of entreprenuership and family enterprise at Insead, panellists discussed how they expect AI regulations to evolve in the future.
Norberto de Andrade, senior adviser for AI policy at the Digital Currencies Governance Group, noted that regulations are becoming “more innovative”, in tandem with how technology is changing.
“We have examples of regulatory sandboxes… but we have more and more of these soft law instruments, such as codes of conduct and codes of practice, that sometimes can be coupled with enforcement mechanisms,” he explained. “So we see more and more ways to go about regulation than just the ivory tower type of legislation work, and I think that is very promising and exciting for people that are working on policy, AI and other emerging technologies.” He added that it is important to have regulations that address the effects, behaviours, outcomes and consequences of technology, regardless of the type used.
Similarly, Marco Dal Lago, chief expansion officer at fintech company Tether, said that when it comes to developing regulations, “if we understand very well why we are regulating, then we can understand how – and also when – to regulate”.
