As 2025 comes to an end, QA Financial reviews in this four-part series the year that reshaped quality assurance in financial services, examining the forces that influenced testing, software risk management, and digital resilience. Part II can be found here. Part III and IV to follow.
This year, quality assurance and software testing for banks and financial services have moved into a phase of deep transformation driven by artificial intelligence.
Throughout the year, news on QA Financial’s news site highlighted how institutions, regulators, vendors, and QA teams are navigating an evolving landscape where AI is reshaping testing strategies, expanding roles and responsibilities, and challenging traditional approaches to quality.
At the core of this transformation is the adoption of AI-enabled testing tools, which have increasingly become strategic assets rather than experimental add-ons.
Banks have accelerated the integration of artificial intelligence into their QA practices to keep pace with the speed and complexity of modern software delivery.
According to industry data discussed on our site earlier in 2025, financial institutions are integrating AI into testing workflows to improve efficiency, compliance, and reliability as digital transformation accelerates across cloud, IoT and generative AI initiatives. This strategic shift reflects a broader commitment to using advanced technologies to tackle quality challenges head-on.
For QA practitioners, this shift means that quality engineering is no longer a siloed, post-development activity.
Teams are embedding AI logic into continuous integration and delivery processes to generate test cases, execute scenarios at scale, and monitor complex systems in near real-time.
Generative AI tools now assist in test creation, augmenting human expertise by translating plain-language requirements into executable tests and scooping up edge cases that manual scripts often overlook.
This reshapes the QA role from manual execution toward supervision of large-scale intelligent testing systems that work in concert with development pipelines.
Industry analysts and QA leads have pointed out that financial services face unique pressures. Banks are accountable not merely for uptime and performance but for regulatory compliance, risk containment, and protecting customer trust.
As a result, QA teams are turning to AI not simply to accelerate delivery cycles but to build assurance frameworks that can test not just traditional functionality but also model behaviour, fairness, and data integrity under regulatory scrutiny.
“We’re entering a new era in software engineering, and AI is at the centre of it.”
– Deloitte
Thought leaders from consulting and technology firms have underscored this shift in perspective. As one Deloitte expert on the intersection of technology and QA observed mid-year: “We’re entering a new era in software engineering, and AI is at the centre of it.”
Within this context, generative tools are helping to automate significant parts of the QA process. The expert explained that “natural language processing is now being used to convert business requirements into test scenarios, catching inconsistencies and gaps early,” and that banks are seeing measurable gains in both speed and test coverage by leveraging AI.
Such observations encapsulate a broader industry trend: quality is no longer exclusively about finding bugs but about understanding software behaviour in complex, data-driven ecosystems.
AI systems are increasingly embedded into banking workflows, from fraud detection to customer onboarding, and QA teams are tasked not just with validating code but with testing the integrity of machine learning models and the systems that host them.
Risks and regulatory challenges
Alongside enthusiasm for AI’s potential in QA, regulators and industry bodies have escalated their focus on the risks associated with rapidly evolving AI deployments.
International watchdogs have publicly warned that deeper integration of AI into financial systems, including quality assurance processes, poses systemic risks if not governed properly. As one multilateral finance body recently noted, vulnerabilities can escalate if testing does not keep pace with the complexity of AI models and the interconnected systems they influence.
These risks include inadequate model governance, poor data quality, and overreliance on third-party solutions, all of which point directly to core testing and assurance challenges.
These regulatory concerns also underscore the growing expectation that QA teams will be accountable not just for defects and performance but for governance around model behaviour, data management, and risk mitigation.
For software testing teams in financial services, regulatory risk, from definitions of AI to compliance frameworks, has become an integral part of quality planning. QA processes now must demonstrate not only detection of bugs, but also robust oversight of model decisions, data lineage, and adherence to evolving regulatory standards.
Singapore leads the way
One particularly notable development this year has been the rise of Singapore as a global hub for AI-led QA innovation in financial services.
Driven by coordinated leadership from the Monetary Authority of Singapore and major banks like DBS, OCBC, and UOB, Singapore is actively building an ecosystem that embeds AI and digital resilience into the financial core.
The city-state’s push includes significant commitments to AI model governance, infrastructure testing, and integration with regulatory frameworks, encouraging banks to deploy hundreds of AI models and positioning the nation as a centre of excellence.
This movement reflects an understanding that QA must encompass not only functional testing but also the assurance of complex AI and cloud systems at scale.
As a QA leader in the region has articulated: “AI-readiness and adoption varies hugely across financial institutions,” and by bolstering development, testing, and deployment capabilities, Singapore aims to set consistent standards for quality assurance in finance.
Rise of intelligent QA
The practical implication for QA teams is clear: automation alone is no longer sufficient. While automated test frameworks and DevOps practices are foundational, they must now be extended to include intelligent, adaptive systems capable of validating AI logic, testing model drift, and ensuring continuous compliance.
Shift-left strategies are gaining traction as part of mature testing practices, embedding more rigorous testing and early validation into development cycles to catch defects and model issues sooner.
Early testing significantly reduces risk exposure and aligns compliance activities with engineering workflows, enabling QA to become a strategic partner rather than a downstream validator.
This shift also affects how teams think about testing environments, test data management, and observability.
As QA becomes increasingly intertwined with performance engineering, security, and compliance functions, testing environments must accurately reflect production conditions and support advanced scenarios, such as model retraining and cloud-native microservices.
The developments of 2025 reveal a clear narrative: QA in financial services is being reshaped by AI, not just as a tool, but as a transformative force.
Quality assurance is now a strategic discipline that intersects with risk management, regulatory compliance, and enterprise resilience.
For QA and software testing teams in banking and finance, this places a premium on new skills, deeper collaboration with security and data science teams, and tighter integration with business and regulatory objectives.
What lies ahead is a future where testing infrastructure is as dynamic as the software it evaluates and where QA leadership will be measured not just by defect counts, but by the ability to assess, govern, and assure complex AI-driven systems within an accountable and resilient framework.
2025 has set the stage; the next phase will demand that QA teams embrace intelligence, risk awareness, and strategic influence at the heart of financial innovation.
As 2025 comes to an end, QA Financial reviews in this III-part series the year that reshaped quality assurance in financial services, examining the forces that influenced testing, software risk management, and digital resilience. Part II can be found here.
Why not become a QA Financial subscriber?
It’s entirely FREE
* Receive our weekly newsletter every Wednesday * Get priority invitations to our Forum events *
REGULATION & COMPLIANCE
Looking for more news on regulations and compliance requirements driving developments in software quality engineering at financial firms? Visit our dedicated Regulation & Compliance page here.
READ MORE
- Leapwork engineering head: Why test automation so often fails to deliver
- World Economic Forum warns financial sector must strengthen AI risk controls
- How Banca Progetto is hard-wiring quality into Italy’s digital banking space
- How Wealthsimple builds quality into the product, not around it
- Digital revamp puts spotlight on internal controls at China Construction Bank
WATCH NOW
QA FINANCIAL PODCASTS
Listen to Sudeepta Guchhait on Nasdaq’s new Mimic AI testing platform
QA Financial sits down with Sudeepta Guchhait, Senior Director of Product Framework & Quality Engineering at Nasdaq
——–
Listen to Wesley Scheffel and Robin Rain on Schroders’ DevOps strategy
We catch up with Wesley Scheffel, Head of Cloud Platform and Product Engineering at Schroders, and Robin Rain, Head of Cloud Platform Architecture
——–
Listen to Citi’s Jason Morris on Lightspeed and the future of continuous delivery
Jason Morris, Head of Developer Pipelines for Securities Markets and Banking at Citi, talks about Lightspeed
