In a survey released in November 2025, Finastra’s Financial Services State of the Nation 2026 report found that only 2 % of financial institutions worldwide reported no use of artificial intelligence (AI). The study, which interviewed 1,509 senior executives across 11 markets, indicates that AI has become a standard component of banking and financial operations.
Key Findings on AI Integration
Six out of ten institutions reported an improvement in their AI capabilities during the previous year, and 43 % identified AI as the most important innovation lever for their organization. AI applications span fraud detection, document intelligence, compliance automation, and customer engagement, embedding itself across the entire financial value chain. Because deployment is now common, the focus has shifted from whether to adopt AI to how to scale it responsibly and govern it effectively.
Primary Use Cases
The survey identified four use cases where institutions are either running programs or piloting AI solutions: risk management and fraud detection (71 %), data analysis and reporting (71 %), customer service and support assistants (69 %), and document intelligence management (69 %). These functions are central to competitive positioning in the financial sector.
Emerging Priorities
Looking ahead, institutions are prioritizing AI‑driven personalization, agentic AI for workflow automation, and AI model governance and explainability. The latter has become a regulatory and reputational imperative as AI decisions grow more consequential and subject to scrutiny.
Infrastructure and Talent Challenges
Despite high adoption rates, the report highlights that AI effectiveness depends on underlying systems. Seventy‑seven percent of institutions plan to invest in modernization over the next 12 months, driven by the need to scale AI. Cloud adoption, data platform modernization, and core banking upgrades are accelerating as foundational layers that determine AI performance.
Talent shortages remain a significant barrier, cited by 43 % of institutions. The challenge is most acute in Singapore (54 %), the United Arab Emirates (51 %), Japan and the United States (both 50 %). Budget constraints also influence strategy; 54 % of respondents are turning to fintech partnerships as a default modernization approach to mitigate costs.
Regional Variations
Across the Asia‑Pacific region, the data reveal distinct priorities. Vietnam leads with 74 % active AI deployment, driven by the urgency of financial inclusion and the need for faster payment and lending processing. Singapore is aggressively scaling cloud and personalization investment, with planned spending increases above 50 % year‑on‑year. Japan remains the most cautious market surveyed, with only 39 % reporting active AI deployment, reflecting legacy constraints and a cultural preference for incremental change.
Governance and Accountability
Agentic AI—systems capable of autonomous decision‑making and multi‑step task execution—is already in use or under pilot by 63 % of institutions. This technology raises significant questions of accountability, transparency, and control. For enterprise leaders, the focus for the coming year is less on whether to invest in AI and more on how to do so in a manner that satisfies regulators, customers, and boards.
Chris Walters, CEO of Finastra, noted that institutions are expected to move quickly but also responsibly, as regulatory scrutiny increases and customers demand financial services that work reliably, securely, and personally every time. The tipping point has been crossed; how institutions manage momentum and governance will shape the competitive landscape for the next decade.
Future Outlook
With AI adoption now near‑universal, the next phase will involve scaling AI responsibly, strengthening governance frameworks, and addressing talent and infrastructure gaps. Institutions that successfully navigate these challenges are likely to gain a competitive advantage, while those that lag may face regulatory penalties and reputational damage. The industry will continue to monitor regulatory developments and technological advancements to ensure that AI deployment aligns with both business objectives and societal expectations.
