Singapore’s artificial intelligence (AI) financial hub strategy rests on a demanding claim: the city-state cannot win by scale, but it can compete by making trust useful. In banking terms, that means giving financial institutions the confidence to test AI, govern it, protect client data, embed it into core systems and scale it across borders without weakening resilience or accountability. The challenge is whether Singapore can make that trust practical enough to shape how banks, insurers, asset managers, fintechs and regulators deploy AI in real operating environments.

At a leadership dialogue, Gan Kim Yong, Singapore’s deputy prime minister and minister for trade and industry, and Tan Su Shan, chief executive officer of DBS, showed how that strategy is being framed at both national and institutional levels. Singapore’s long-standing reputation for reliability must now serve the more demanding purpose of giving financial institutions enough confidence to experiment with AI while maintaining the discipline to manage risk.

"Our relevance has never rested on size,” said Gan. “It rests on whether we are trusted, useful and connected to the world." Tan sharpened the shift by recasting Singapore’s traditional value proposition. The country has long been trusted because "we don’t make mistakes.” In the AI era, she argued, it must also be trusted because it is "the safest place for you to make mistakes," a call to turn what she described as Singapore's “kiasu” (scared to lose) and “kiasi” (scared to take risks) culture into a “can do” culture.

DBS’ Global AI Financial Hub Index places Singapore third among 15 global financial hubs, behind New York and San Francisco. New York leads on AI capability, capital markets depth and institutional scale, while San Francisco has exceptional AI talent and innovation capacity. Singapore sits behind both on capability depth and scale, but it has stronger advantages in the systems that help banks manage risk, align with regulators and build client confidence. That difference explains why trust, rather than size, sits at the heart of Singapore’s AI finance strategy.

Singapore must make trust work inside core banking systems

Singapore must first prove that trust can move from national reputation into institutional execution. DBS is the strongest evidence because it has moved AI from pilots into production. The bank reported SGD 750 million ($555 million) in economic value from AI in 2024 and SGD 1 billion ($740 million) in 2025. By early 2026, it had embedded generative and agentic AI across credit workflows, customer servicing, operations and cybersecurity.

Many banks have experimented with generative AI, analytics tools or agentic workflows, but fewer have redesigned core operating models around them. Gan described the challenge directly: "The promise of AI in finance is clear. But its full value will only be realised when it is embedded into workflows, risk systems, and operating models."

Tan noted that DBS approaches adoption through cultural change and operational redesign. "We create controlled spaces for people to experiment and try," she said, citing a session in which 300 members of DBS’ leadership team built their own AI agents. The exercise changed how senior leaders understood the technology. "Everyone realised it is not hard to build your own agents, and this mindset shift was a game changer."

Singapore's trust claim holds only if adoption extends beyond DBS. The bank has advantages in data, technology, governance and talent that smaller institutions cannot easily replicate. Other financial firms will also need credible production pathways that boards, regulators and clients accept. Otherwise, Singapore risks becoming a strong AI showcase led by a few champions rather than a financial hub with system-wide AI capability.

Resilience belongs inside this institutional test because confidence in AI finance will fail if systems cannot withstand stress. Client data, payments, compliance systems and risk models leave little room for consumer-style experimentation. “We have a responsibility to keep our customers’ money and data safe," Tan said.

Gan argued that Singapore’s credibility as an AI financial hub will depend on whether firms can explain AI-assisted decisions, recover from outages, protect clients during attacks and maintain alternative channels when automated systems fail. "For companies with critical systems, you have to think about security, you have to think about continuity,” he said. “Always have a backup plan."

Capability must spread beyond the largest institutions

Singapore's strategy must extend beyond the largest banks into the wider ecosystem of insurers, asset managers, fintechs, small and medium enterprises (SMEs), vendors and service providers that support banking and capital markets activity. If only its biggest players can afford the people, systems and controls needed to deploy AI safely, the country's AI finance advantage will remain narrow.

Gan framed Singapore’s approach as ecosystem-led because AI deployment depends on more than the readiness of individual institutions. "Our advantage is not scale," he said. "We will not outspend the largest economies, or build the biggest models." Singapore’s advantage, he argued, lies in bringing regulators, banks, insurers, asset managers and technology firms together to test AI against real-world financial use cases while managing the risks around data, governance and operations.

Centres of Excellence (CoEs) are the practical mechanism, bringing together AI expertise, computing capacity, consultants and knowledge partners so firms can test AI solutions without having to build every capability from scratch. Gan cited the CoE for AI in manufacturing as an example of how smaller firms can tap shared resources, experiment with AI adoption and build confidence before developing their own capabilities. Likewise, in finance, banks need clients, suppliers and ecosystem partners that can generate reliable data, adopt digital workflows and manage AI-enabled risks.

Kampong AI, the government's planned AI park at One-North run by JTC, complements the CoEs by clustering AI startups, researchers and industry partners in shared work and living space. It is broader than finance, with early occupants including robotics firms, the National Robotics Programme's Embodied AI Centre and the Singapore AI Safety Hub. Larger institutions also carry an obligation to raise capability beyond their own walls. Gan urged companies with in-house CoEs to open them to clients, suppliers and partners: "If you are able to level up the AI capability in your ecosystem, eventually it will benefit you as a member, as a player in the ecosystem."

Workforce transition is the other half of the ecosystem argument. Safe AI adoption depends on whether people can use, supervise and challenge the technology responsibly. Gan acknowledged the anxiety this is generating, particularly among graduates entering AI-exposed fields. "This sense of anxiety is not unhealthy; they ought to be aware of the changing environment." Awareness becomes productive if institutions create credible pathways into new forms of work.

Training is therefore central to Singapore’s competitiveness in AI finance. "If we slow AI adoption, we will weaken our competitiveness and ultimately hurt workers more, not less," Gan said. Singapore’s response includes SkillsFuture, AI literacy programmes and sector-specific training to help workers move into AI-enabled roles.

Gan also called on employers to treat training as a business imperative: "If your workforce is not training and upgrading, very soon you will find that you fall behind." Tan made the same point from an organisational culture perspective. "Bring your people with you," she said of DBS’s experience with AI adoption, noting that explaining the "why" of change matters as much as communicating the "what" and the "how."

Singapore’s model must work across ASEAN

Singapore’s AI finance model also needs to work beyond its domestic market. Asia's financial activity is regional by nature, with capital, clients, supply chains, payments and wealth moving across markets with different legal systems, regulatory expectations and data rules. A model governed in Singapore but unable to operate across regional data, identity, payments and compliance systems will have limited commercial reach.

The ASEAN Digital Economy Framework Agreement (DEFA) gives Singapore’s strategy a regional channel. DEFA aims to make digital rules, data flows and governance standards more consistent across ASEAN, whose digital economy stands at around $300 billion today and is projected to reach $1 trillion by 2030. Negotiations reached substantial conclusion in late 2025, with signing expected in 2026. For Singapore, DEFA extends the conditions for safe AI adoption beyond the domestic market and makes AI-enabled financial services portable across the region.

Gan said ASEAN members should look at how they can "develop AI systems jointly, help each other, leverage each other's strengths and level up AI capability." Singapore's advantage becomes more valuable when it can move with clients, capital and digital services. Regional portability will determine whether Singapore’s AI finance model remains a domestic strength or becomes a usable platform for banks operating across ASEAN.

Among global hubs, Singapore’s position differs. New York has unmatched scale and capability but must translate that activity into a clearer trust signal. Mumbai has world-class digital public infrastructure and significant AI adoption, particularly in payments and digital finance, but still needs stronger governance formalisation and cross-border recognition. Abu Dhabi has built trust infrastructure ahead of market depth, so its challenge is scaling institutional adoption and talent density. Shanghai offers a state-directed alternative, with strong capability but a trust model resting on different assumptions about international acceptance.

Singapore will not outspend New York, match Mumbai's domestic scale or replicate Shanghai's state-directed model. What it can do is build a coherent open-market environment for trusted AI finance and make that environment work across regional borders. That is a narrower strategy than the US or China can pursue, but it plays to Singapore's strengths in institutional depth, regulatory coherence and digital infrastructure.

The promise is credible, but the evidence must broaden

Singapore’s case rests on institutional depth, clear regulation, digital public infrastructure, crisis-tested public administration and a financial sector accustomed to close coordination among regulators, banks and technology providers. 

Yet, adoption still needs to move beyond DBS and the largest institutions into the wider financial system. CoEs, Kampong AI and employer-led training will have to give smaller firms and workers enough capability to participate in AI-enabled finance. System resilience will also become more important as AI moves deeper into payments, compliance, cyber defence and risk systems. Across ASEAN, DEFA and similar regional arrangements will determine whether Singapore’s model can operate in markets with different levels of digital and regulatory maturity.

Singapore’s strategy is plausible, but not yet proven. The evidence is strongest at DBS and among Singapore’s largest institutions, so the next proof point must be system-wide adoption. The harder tests sit further out, in whether ASEAN regulatory convergence delivers cross-border portability before larger hubs close their governance gaps, and whether Singapore's mid-career financial workforce can reskill at the pace AI deployment requires.