As banking institutions scramble to digitise, automate, and modernise, software testing and quality assurance have quietly become the foundations of operational resilience and customer satisfaction.
In a sector known for complexity, regulation, and legacy infrastructure, effective QA is no longer a luxury, it’s a competitive necessity.
While the banking industry is pouring record sums into technology, averaging more than 10% of revenues, according to Boston Consulting Group’s internal benchmarks, much of this spend still goes toward “run-the-bank” activities rather than transformational, innovation-driven efforts.
That includes testing environments that are fragmented, over-customized, and slow to deliver value. The consequences are rising QA costs, inconsistent software quality, and sluggish release cycles, all of which hinder digital innovation.
“Today, more than 60% of overall tech spend is allocated to RTB activities, diverting resources from innovation and transformation efforts,” explained Romary Barbey, Managing Director and Partner at BCG in Paris, in a recent sector analysis.
The message is clear: banks need to shift from fragmented, reactive QA practices to streamlined, automated, and intelligence-driven testing.
The result is not just better code, but better customer experiences, stronger compliance, and faster innovation.
Barbey and Luc Grimond, a senior partner at BCG in Singapore, argue that this transformation starts with structural change, specifically, simplifying tech stacks and improving collaboration between business and IT teams. This helps QA evolve from a bottleneck into a business enabler.
“An effort to streamline and harmonise [customer journeys] where sensible, such as when consistency is desired or efficiency can be achieved, can drive meaningful simplification,” stressed Barbey, referring to the potential of standardized platforms and shared testing environments.
One powerful example cited in the report is a large European bank that consolidated onboarding flows across its business lines.
This shift led to a 50% to 80% reduction in cost, depending on the product or geography. Behind that transformation was a restructured architecture of shared services and testing pipelines that emphasized reusability, API-driven integration, and cross-product QA standards.
“Simplifying the tech stack helps banks to reduce RTB costs and expand the share of CTB investment.”
– Romary Barbey
Equally critical is the need to cut unnecessary testing complexity.
“Redundant tools and licenses due to lack of centralized governance and excessive customisation” are key culprits, noted Barbey. Overlapping software, outdated scripts, and duplicated test cases bloat QA budgets and reduce time to value.
BCG encourages banks to adopt what it calls a “build-to-test” approach: start with functional components, then automate testing using integrated pipelines.
According to Grimond, this reduces time-to-market and defect rates.
“Promising moves include the deployment of pipelines and tools with automated security and vulnerability checks and the use of a build-to-test approach under which a functional piece of code is built first and then followed by testing to ensure quality and functionality.”
Artificial intelligence is already revolutionising QA in finance. Agentic AI, in particular, is emerging as a force multiplier in automating repetitive testing tasks, accelerating regression cycles, and improving release confidence.
“Agentic AI workflows can simplify the technology stack by removing costly, redundant software-as-a-service applications,” said Grimond.
He pointed out that QA teams can deploy GenAI to generate synthetic data, automate code reviews, and predict the failure likelihood of individual modules, transforming how software is tested and released.
This, in turn, enables teams to deploy faster, test smarter, and reallocate effort to strategic business change rather than firefighting.
“Ultimately, simplifying the tech stack can help banks reduce RTB costs and expand the share of CTB investment,” Barbey argued.
Compliance
Compliance is another area where software testing has become critical. With regulatory oversight tightening in the wake of system outages and security failures, banks are under pressure to prove not only that their software works, but that it can withstand stress, simulate crisis, and produce clear, auditable logs.
This is driving demand for “digital twins”, virtual replicas of banking systems that allow institutions to run automated stress tests and simulate attacks, outages, or partner failures. These simulations rely on robust QA infrastructure to validate outputs and predict failure points across systems.
“Banks leveraging regulatory constraints as transformation opportunities can develop critical capabilities, including risk monitoring, and enable faster compliance and decision-making,” Grimond said.
Leading banks are also deploying AI to improve data lineage, automate documentation, and monitor QA coverage.
For example, “a global financial institution recently leveraged generative AI to automate data lineage capture and metadata generation, achieving 40% to 70% productivity gains in specific tasks,” Barbey disclosed.
But it’s not just tools and automation that matter, it’s the people behind them. Grimond pointed out that banks are under-investing in QA talent and facing a critical skill gap.
“Banks are increasingly focused on building their own internal IP, prompting many to insource strategic skills.”
– Romary Barbey
However, two-thirds of banks admit to having weak value propositions for digital talent, and most are still reliant on external contractors for core testing operations.
To address this, BCG recommends a skill-based workforce strategy that emphasizes developers and test engineers over managers and orchestrators.
The goal is to push the proportion of technical talent, particularly QA engineers, upward toward 70% or even 80% in leading institutions.
Grimond emphasized that this transformation requires “reshaping the end-to-end talent strategy, which includes defining the requisite skills, hiring, onboarding, and training to be built around skills rather than roles.”
Testing infrastructure
Finally, BCG argues that testing infrastructure must evolve from static cost centers to modular, service-driven platforms.
Testing should be delivered “as-a-service” within banks, with standardized test environments, self-service tools, and API integrations that allow business teams to run simulations, validate code, and track QA outcomes without depending on manual QA cycles.
“Banks must transform IT into a set of standardised, modular on-demand services,” Barbey observed.
That includes test automation services, security validation modules, data masking tools, and integration test beds, all accessible via internal portals or cloud interfaces. Monitoring tools should provide real-time feedback on test execution, bottlenecks, and failure rates, helping banks make smarter release decisions.
For banks ready to break from legacy habits, these modern QA architectures unlock speed, compliance, and confidence.
“Across industries, technology and business will only become more intertwined in the future, and banking is no exception,” Grimond concluded.
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