AI deployment in financial services has crossed a critical threshold, with only 2% of institutions globally reporting no AI use whatsoever—a dramatic indicator that the technology has moved decisively from boardroom discussion to operational reality.
New research from Finastra surveying 1,509 senior leaders across 11 markets reveals that Singapore financial institutions are leading this transition, with nearly two-thirds already deploying AI in production environments rather than confining it to experimental pilots.
The Financial Services State of the Nation 2026 report shows 73% of Singapore institutions have deployed or improved AI use cases in their payments technology over the past 12 months—nearly double the 38% global average.
“Singapore institutions are showing what AI execution at scale really looks like. This is not about isolated pilots. It is about embedding AI into core operations, supported by modern infrastructure, strong data foundations, and disciplined governance,” said Chris Walters, CEO of Finastra.
From experimentation to enterprise AI deployment
Globally, 31% of institutions report scaled deployment across multiple functions, while 30% have achieved limited production deployment. A further 27% are piloting or testing in limited functions, with only 8% still in the exploration phase.
This represents a fundamental shift in how AI deployment is approached within financial services. The technology is no longer confined to innovation labs or proof-of-concept projects but has become integral to core banking operations.
In Singapore specifically, an additional 35% are piloting or researching AI applications beyond their current production deployments, indicating a robust innovation pipeline that positions the city-state as a regional AI leader.
The primary objectives driving this deployment vary by market. In Singapore and the US, 43% of institutions are using AI to improve compliance and regulatory processes—reflecting the technology’s ability to navigate increasingly complex oversight requirements while maintaining operational resilience.
Globally, the top AI implementation objectives are improving accuracy and reducing errors (40%), increasing employee productivity (37%), and enhancing risk management capabilities (34%). Vietnam prioritises speed, with 49% using AI to accelerate processing in payments and lending services, while Mexico emphasises customer experience and personalisation at 43%.
Cloud infrastructure enables AI at scale
Singapore’s AI deployment success is underpinned by advanced cloud adoption. The research shows 55% of Singapore institutions host all or most infrastructure in the cloud, with a further 30% operating hybrid environments—an 85% total that significantly exceeds many global peers.
This cloud-first approach provides the scalable, resilient infrastructure required for enterprise AI deployment. Without modern data architectures and elastic compute capabilities, AI remains confined to small-scale experiments that cannot deliver enterprise-wide value.
The link between modernisation and AI deployment is clear in the data. Nearly nine in ten institutions (87%) globally plan to increase modernisation investment over the next 12 months, with Singapore leading in planned spending increases above 50%.
Institutions also report strong confidence in their technology foundations, with 71% of Singapore respondents rating their core infrastructure, security and reliability ahead of peers—the highest globally and well above the 72% average.
Security spending surges as AI creates new threat vectors
As AI deployment accelerates, so do AI-enabled security threats. The research projects a 40% average increase in security spending globally in 2026, with institutions responding to what 43% describe as constantly 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, underscoring the city-state’s recognition that AI-powered fraud requires 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 globally—enabling real-time threat monitoring and automated response at scale.
Multi-factor authentication and biometrics deployment reached 54% in Singapore, as institutions strengthen identity verification against increasingly sophisticated attack vectors that leverage generative AI and deepfake technologies.
Looking ahead, API security and gateway hardening emerge as a key priority, cited by 34% globally as a focus area for the next 12 months. This reflects growing recognition that as ecosystems expand and AI systems interact across organisational boundaries, securing access points becomes paramount.
Talent shortages emerge as the primary barrier
Despite strong progress, barriers to AI deployment persist. Talent shortages top the list globally at 43%, but in Singapore this figure reaches 54%—the highest of any market surveyed and tied only with the UAE.
This intense competition for specialised AI, cloud, and security expertise reflects the gap between institutional ambition and available human capital. Demand for professionals who can architect AI systems, ensure model governance, and integrate AI into existing workflows far outpaces supply.
Budget constraints also weigh heavily, cited by 52% of Singapore institutions—again, the highest globally. Even well-funded organisations face difficult prioritisation decisions as they balance AI deployment, security investments, modernisation, and customer experience initiatives.
In response, 54% of institutions globally are partnering with fintech providers as their default approach to accessing AI capabilities without bearing the full burden of talent acquisition or system development. These partnerships allow organisations to accelerate AI deployment while maintaining control over critical data and compliance requirements.
The research reveals a sector that has decisively crossed the AI adoption threshold but now faces the more complex challenge of scaling responsibly. As Walters noted, success will be defined not by the breadth of AI experiments but by the ability to embed intelligence into operations while strengthening rather than compromising trust.
The study surveyed managers and executives from institutions across France, Germany, Hong Kong, Japan, Mexico, Saudi Arabia, Singapore, the UAE, the UK, the US and Vietnam, representing organisations that collectively manage over $100 trillion in assets.
(Photo by Peter Nguyen)
See also: AI Expo 2026 Day 2: Moving experimental pilots to AI production
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