AI use in financial services is approaching universality. Finastra’s 2026 survey of 1,509 senior executives across 11 markets found that only 2% of institutions report no AI use. Six in ten improved capability during the previous year, and 43% identify AI as their most important innovation lever.
Adoption alone therefore says little about competitive advantage. The meaningful difference is whether institutions can scale systems across core functions, govern consequential decisions, and produce measurable outcomes.
The leading use cases show how deeply AI has moved into operations: risk management and fraud detection at 71%, data analysis and reporting at 71%, customer-service assistants at 69%, and document intelligence at 69%.
Core Workflows Raise the Accountability Standard
These systems influence money, access, compliance, and customer treatment. A document tool that misreads evidence or a fraud model that blocks legitimate activity creates consequences beyond employee inconvenience.
The next priorities include personalisation, agentic workflow automation, model governance, and explainability. As agents take multi-step actions, institutions need to show which data, model, policy, and approval produced the result.
Governance should be executable through permissions, thresholds, monitoring, and mandatory escalation rather than stored in a policy document. Customers need clear human routes, while employees must be able to challenge outputs without working around the system invisibly.
Evaluation belongs at workflow level. Fraud losses, false declines, document corrections, service resolution, compliance outcomes, and human review time reveal value more accurately than the number of models deployed.
Sixty-three percent of institutions are running or piloting agentic programmes. Broader autonomy should depend on evidence, not on the market’s momentum.
Modernisation Determines How Far AI Can Scale
Eighty-seven percent plan to invest in modernisation during the next year. Cloud adoption, data platforms, and core-banking upgrades are being treated as foundations for AI rather than separate infrastructure projects.
Fragmented records and ageing interfaces constrain models. A pilot can work from a curated dataset, while production must handle identity, permissions, real-time updates, exceptions, and resilience across the institution.
Modernisation should create authoritative data and reusable controls, not simply move legacy complexity into a hosted environment. Institutions need clear lineage, recovery, security, and exit options as models become embedded in essential operations.
Talent remains the largest barrier, cited by 43% globally and higher proportions in Singapore, the UAE, Japan, and the US. Fintech partnerships are the default modernisation strategy for 54%, helping organisations access capability without building everything internally.
Partnership does not outsource accountability. Banks still own customer outcomes, regulatory compliance, and the ability to operate if a provider fails.
Regional Priorities Produce Different Adoption Paths
Vietnam leads active deployment at 74%, driven by financial inclusion and faster payment and lending processes. Singapore is increasing cloud and personalisation investment aggressively. Japan reports active deployment at 39%, reflecting legacy constraints and a more incremental approach.
These differences show why a single global maturity ranking can mislead. Market structure, infrastructure, regulation, talent, and customer need determine which pace and use case are appropriate.
Institutions should compare themselves on outcomes and control, not only deployment percentages. A cautious production system with strong evidence may be more mature than a large portfolio of disconnected pilots.
Near-universal adoption changes the boardroom question. Leaders no longer need to prove that financial AI exists. They need to prove that it works reliably, securely, and fairly at scale.
The competitive advantage will come from the less visible capabilities beneath the model: modern data, resilient architecture, skilled employees, clear governance, and disciplined integration.
Finastra’s survey marks a tipping point, but not the end of the journey. When everyone uses AI, the winners are not those who announce it most loudly. They are those whose customers experience better financial services without having to wonder whether automation weakened the trust on which those services depend.
