98% of Financial Institutions Now Use AI

The debate over whether financial institutions should adopt AI is over. According to Finastra's Financial Services State of the Nation 2026 report, surveying 1,509 senior leaders across 11 markets representing over $100 trillion in assets, just 2% of institutions globally report no AI use whatsoever. The question has shifted decisively from "if" to "how fast."

And the answer varies dramatically by geography.

Singapore Sets the Pace

Singapore's financial sector has emerged as the global benchmark for AI execution. Nearly two-thirds of Singapore institutions are already deploying AI in production environments, not confined to innovation labs or proof-of-concept projects, but embedded in core banking operations.

The numbers are striking: 73% of Singapore institutions have deployed or improved AI use cases in payments technology over the past 12 months, nearly double the 38% global average. An additional 35% are piloting or researching applications beyond their current production deployments. Zero institutions reported no plans to adopt AI.

This isn't accidental. Singapore's AI leadership is underpinned by advanced cloud infrastructure — 85% of institutions host all or most of their infrastructure in the cloud or operate hybrid environments. That compares favorably to many global peers and provides the elastic compute and modern data architecture required to move AI from experiment to enterprise-wide capability.

The U.S. is not far behind. American institutions report 65% active deployment (versus 61% globally), and 42% plan to increase AI investment by more than 50% in 2026. Vietnam leads in raw deployment rates at 74%, while Japan trails at 39%, a reflection of its preference for incremental innovation over rapid transformation.

Where the Money Goes

The deployment priorities reveal distinct strategic orientations across markets. In Singapore and the U.S., 43% of institutions are deploying AI to improve compliance and regulatory processes, using the technology to navigate increasingly complex oversight requirements while maintaining operational resilience.

Globally, the top implementation objectives tell a broader story: improving accuracy and reducing errors (40%), increasing employee productivity (37%), and enhancing risk management (34%). Vietnam prioritizes processing speed, with 49% using AI to accelerate payments and lending. Mexico emphasizes customer experience and personalization at 43%.

The most widely adopted use cases, risk management, fraud detection, data analysis, and customer service, are the functions where AI's ability to process volume, identify patterns, and operate continuously creates the most immediate operational value.

The Security Paradox

As AI deployment accelerates, it creates a paradox: the same technology that strengthens institutional defenses also arms increasingly sophisticated attackers.

Financial institutions project a 40% average increase in security spending in 2026. Singapore again leads, 62% of institutions have implemented or upgraded advanced fraud detection and transaction monitoring in the past year (versus 48% globally), and 60% have modernized their Security Information and Event Management (SIEM) and Security Orchestration, Automation and Response (SOAR) capabilities, the highest rate of any market surveyed.

Multi-factor authentication and biometric deployment reached 54% in Singapore, as institutions strengthen identity verification against attack vectors leveraging generative AI and deepfake technologies. Looking forward, API security and gateway hardening emerge as the next critical priority, cited by 34% globally, reflecting the reality that as AI systems interact across organizational boundaries, securing those access points becomes paramount.

The Talent Bottleneck

Despite near-universal adoption, the biggest constraint isn't technology — it's people. Talent shortages top the barrier list globally at 43%, but in Singapore the figure reaches 54%, tied with the UAE as the most acute talent gap of any market surveyed.

The demand for professionals who can architect AI systems, ensure model governance, and integrate AI into existing workflows far outpaces supply. Budget constraints compound the challenge, cited by 52% of Singapore institutions, the highest globally, as even well-funded organizations face difficult trade-offs between AI deployment, security investment, modernization, and customer experience.

The industry's response has been pragmatic: 54% of institutions globally now partner with fintech providers as their default approach to accessing AI capabilities. These partnerships allow organizations to accelerate deployment without bearing the full burden of talent acquisition or in-house system development.

The Execution Gap Is What Matters Now

The 98% adoption figure is headline-worthy, but it obscures the more meaningful metric: only 31% of institutions have achieved scaled deployment across multiple functions. Another 30% have limited production deployment. Nearly a third are still piloting or testing.

The gap between having AI and having AI that transforms operations is where competitive advantage will be determined over the next 12 to 24 months. Singapore's playbook, cloud-first infrastructure, disciplined governance, aggressive security investment, and strategic fintech partnerships, offers a template. But with 87% of institutions globally planning to increase modernization investment this year, the race to close that execution gap is accelerating across every market.

The institutions that figure out how to scale AI responsibly, maintaining security, governance, and customer trust while deploying at speed, won't just outperform their peers. They'll define the next era of financial services.