Artificial intelligence is no longer sitting at the edge of banking innovation. It is now embedded directly into the operating core of financial institutions, and that shift is forcing QA and software testing teams to confront a new kind of resilience challenge.
In fact, only 2% of financial institutions now report no use of artificial intelligence at all, according to fresh data from Finastra, a mayor supplier of banking technology, describing what it called “a decisive AI tipping point” for the sector.
For testing leaders inside banks, the message is clear: AI adoption is moving beyond pilots, and the burden is shifting toward validation, governance, and operational resilience under regulatory pressure.
Finastra’s 2026 research surveyed senior professionals across 11 global markets and found that AI is no longer treated as optional.
“Artificial intelligence has moved from experimentation to everyday use,” Finastra CEO Chris Walters pointed out, adding: “Institutions are no longer debating whether to adopt AI. They are focused on where it delivers tangible value and how it can be deployed responsibly.”
That pivot matters for QA because AI systems are now influencing workflows that cannot fail safely, fraud controls, onboarding, regulatory reporting, lending decisions, and customer engagement.
Barbara Crestil, founder of StratEdge in Geneva, Switzerland, said the survey confirmed that AI “is embedded in the operating spine of banking” across many domains.
Finastra itself described AI as “the connective tissue of banking, the intelligence layer that links data, channels, and services into something coherent and responsive.”
For software testing teams, that connective tissue becomes a systemic dependency, and therefore a systemic risk.
Governance becomes the test environment
The top AI deployments highlighted in the survey were already deeply operational.
Finastra said the leading use cases included “risk management and fraud detection (71%), data analysis and reporting (71%), customer service and support assistants (69%) and document intelligence management (69%).”
The next priorities were equally governance-heavy: “AI-driven personalisation, agentic AI for workflow automation, and AI model governance and explainability.”
Crestil argued that “where AI is positioned determines where accountability resides.”
That accountability increasingly lands on QA organisations, which must prove not only functional performance but resilience under stress, explainability under scrutiny, and safety across real-world banking conditions.
Alongside AI expansion, Finastra found that security investment is accelerating sharply. Institutions “expect security investment to increase by an average of 40% in 2026,” reflecting “growing digital risk, tighter regulatory scrutiny, and deeper reliance on technology across core operations.”
“Where AI is positioned determines where accountability resides”.
– Barbara Crestil
Inside the report, Finastra warned that “escalating digital threats, particularly those linked to AI, are reshaping priorities,” with 43% citing “constantly evolving risks” and 40% citing “AI deployment itself” as the biggest security challenges.
For QA teams, that means resilience testing is no longer peripheral. It is becoming a frontline requirement.
The report noted that institutions were prioritising “backup, disaster recovery improvements and resilience testing,” particularly in the United States, “ensuring continuity in the face of disruption.”
In practice, this is where AI risk meets operational resilience engineering: testing whether AI-enabled systems can fail safely, recover predictably, and remain compliant during disruption.
Testing challenges in the cloud
Finastra also tied AI readiness directly to modernisation.
“Nearly nine in ten institutions will invest in modernization over the next year,” the report said, emphasising that “modernization has become the backbone of transformation, enabling AI and real-time payments to scale while strengthening the infrastructure that underpins customer experience, security, and operational agility.”
Cloud adoption was positioned as a resilience lever, providing “the scalability and resilience required for modern financial services.”
But for QA organisations, scaling infrastructure means scaling test discipline: validating AI models across legacy integrations, cloud environments, real-time payment rails, and regulatory reporting systems.
Finastra warned that “AI’s potential is only as strong as the systems beneath it.”
“Trust will rest not on the technology, but on the leadership behind it.”
– Barbara Crestil
Meanwhile, Crestil argued that Europe’s advantage will rest not in speed, but in governance.
“EU banks embed AI within governance, explainability and control, enabling scale while preserving institutional trust,” she said.
She pointed to examples including UBS, Deutsche Bank, Lloyds, BNP Paribas and ING, concluding: “A consistent pattern: scale anchored in governance.”
Private banking, she added, reflected “discipline over scale,” where AI is used “selectively to deepen insight, enhance anticipation and tighten risk discipline.”
For QA teams, those disciplines translate directly into model testing, auditability, and resilience validation.
Trust is the outcome, not the assumption
Both Finastra and Crestil returned repeatedly to trust as the ultimate benchmark.
“Technology decisions now sit at the center of trust, resilience, and customer experience,” Walter concluded.
He warned that institutions were “expected to move quickly, but also responsibly, as regulatory scrutiny increases, and customers demand financial services that work reliably, securely, and personally every time.”
Crestil concluded with the same institutional framing: “Trust will rest not on the technology, but on the leadership behind it.”
For QA and software testing teams inside banks, that leadership increasingly depends on whether AI systems can be governed, tested, and made resilient at scale, before failures become regulatory events.
As Finastra put it: “Progress is not about being the biggest or the fastest, but about being the most dependable.”
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