AI deployment in financial services has crossed a critical inflection point. According to Finastra’s Financial Services State of the Nation 2026 report, only 2% of institutions globally report no AI use. The findings, based on a survey of 1,509 senior leaders across 11 markets, show that AI deployment in financial services has shifted from experimentation to production-scale operations, with Singapore emerging as the global leader.
In Singapore, nearly two-thirds of institutions have AI systems running in production environments. Additionally, 73% have deployed or enhanced AI use cases in payments over the past 12 months—almost double the 38% global average. Globally, 31% of institutions report scaled AI deployment across multiple functions, while 30% have limited production rollouts.
Financial institutions are embedding AI into compliance, payments, lending, and risk management. In Singapore and the United States, 43% of institutions prioritize AI for compliance and regulatory automation. Globally, top objectives include improving accuracy (40%), increasing employee productivity (37%), and strengthening risk management (34%).
Cloud infrastructure is accelerating this shift. In Singapore, 85% of institutions operate fully or mostly in the cloud, enabling scalable AI deployment. Nearly 87% of institutions worldwide plan to increase modernization spending in the next 12 months.
Security investment is rising alongside AI adoption. Institutions project a 40% increase in security spending in 2026, with 62% of Singapore firms upgrading fraud detection systems and 60% modernizing SIEM and SOAR platforms.
Despite strong momentum, talent shortages remain the top barrier, cited by 43% globally and 54% in Singapore. Many institutions are partnering with fintech providers to bridge skills gaps while maintaining governance and compliance.
AI deployment in financial services has entered its execution phase. The competitive advantage now depends on disciplined governance, modern infrastructure, and the ability to scale AI responsibly.
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