Singapore Leads Financial Sector in Moving AI Deployments to Production

AI adoption in financial services has passed a critical tipping point. Only 2% of institutions worldwide report no use of AI at all—a clear sign the technology has moved firmly from strategic discussion to everyday operation.

New research from Finastra, surveying 1,509 senior leaders across 11 markets, finds Singapore's financial institutions are at the forefront. Nearly two-thirds are already using AI in live environments, moving beyond limited pilot programs.

The Financial Services State of the Nation 2026 report indicates 73% of Singaporean institutions have deployed or enhanced AI in their payments technology over the past year—almost double the 38% global average.

"Singapore is demonstrating what true, scaled AI implementation entails. This isn't about scattered experiments. It's about integrating AI into core business functions, backed by modern infrastructure, solid data governance, and robust frameworks," said Chris Walters, CEO of Finastra.

From experimentation to enterprise AI deployment

Globally, 31% of institutions report scaled AI deployment across multiple business areas, while 30% have limited production use. Another 27% are running pilots or tests in specific functions, with a mere 8% still in the initial exploration stage.

This marks a fundamental change in the financial sector's approach to AI. The technology has graduated from innovation labs and proof-of-concepts to become a core component of banking operations.

In Singapore, an additional 35% of institutions are piloting or researching AI applications beyond their current deployments, signaling a strong pipeline of innovation that cements the city-state's role as a regional AI hub.

The key goals for AI deployment differ by market. In Singapore and the US, 43% of institutions are leveraging AI to enhance compliance and regulatory processes—highlighting the technology's value in managing complex oversight while maintaining operational stability.

Worldwide, the main objectives for AI implementation are improving accuracy and reducing errors (40%), boosting employee productivity (37%), and strengthening risk management (34%). Vietnam focuses on speed, with 49% using AI to accelerate payments and lending services. Mexico prioritises customer experience and personalisation at 43%.

Cloud infrastructure enables AI at scale

Singapore's AI success is built on widespread cloud adoption. The research shows 55% of its institutions host all or most infrastructure in the cloud, with another 30% using hybrid environments. This combined 85% far surpasses the adoption rates of many global counterparts.

This cloud-first strategy provides the scalable, resilient foundation needed for enterprise AI. Without modern data architecture and elastic computing power, AI initiatives remain small-scale experiments unable to deliver organisation-wide impact.

The connection between technological modernisation and AI deployment is evident. Nearly 90% of institutions (87%) globally plan to increase modernisation spending in the next 12 months, with Singapore leading in planned budget increases exceeding 50%.

Institutions also express strong confidence in their technological foundations. 71% of Singapore respondents rate their core infrastructure, security, and reliability as superior to their peers—the highest confidence level globally and well above the 72% average.

Security spending surges as AI creates new threat vectors

As AI deployment grows, so do AI-powered security threats. The research forecasts a 40% average rise in global security spending for 2026, as institutions respond to what 43% describe as continuously evolving risks.

Singapore leads in deploying advanced fraud detection and transaction monitoring, with 62% having implemented or upgraded these systems in the past year. This compares to a 48% global average, highlighting the local recognition that AI-driven fraud demands AI-powered defences.

Similarly, 60% of Singapore institutions have modernised their Security Information and Event Management (SIEM) and Security Orchestration, Automation and Response (SOAR) capabilities—again the highest rate globally—enabling real-time threat monitoring and automated responses at scale.

Multi-factor authentication and biometrics deployment reached 54% in Singapore, as institutions bolster identity verification against increasingly sophisticated attacks that use generative AI and deepfake technology.

Looking forward, API security and gateway hardening are becoming key priorities, identified by 34% of institutions globally as a focus for the coming year. This reflects a growing understanding that as digital ecosystems expand and AI systems interact across boundaries, securing access points is crucial.

Talent shortages emerge as the primary barrier

Despite significant progress, challenges to AI deployment remain. Talent shortages are the top global barrier at 43%, but in Singapore this figure climbs to 54%—the highest of any market surveyed and matched only by the UAE.

This fierce competition for specialised AI, cloud, and security expertise highlights the gap between institutional goals and available skilled professionals. Demand for experts who can design AI systems, ensure model governance, and integrate AI into workflows vastly exceeds supply.

Budget constraints are another major hurdle, cited by 52% of Singapore institutions—again, the highest rate globally. Even well-resourced organisations face tough prioritisation decisions as they balance AI investment, security upgrades, system modernisation, and customer experience projects.

In response, 54% of institutions worldwide are turning to fintech partnerships as their primary method for accessing AI capabilities without the full burden of hiring talent or developing systems in-house. These collaborations allow organisations to accelerate AI deployment while maintaining control over critical data and compliance.

The research depicts a sector that has unequivocally embraced AI but now confronts the more intricate challenge of scaling it responsibly. As Walters observed, success will be measured not by the number of AI experiments, but by the ability to embed intelligence into operations in a way that builds, rather than erodes, trust.

The study surveyed managers and executives from institutions in France, Germany, Hong Kong, Japan, Mexico, Saudi Arabia, Singapore, the UAE, the UK, the US, and Vietnam. These organisations collectively manage over $100 trillion in assets.

See also: AI Expo 2026 Day 2: Moving experimental pilots to AI production

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