The race to accelerate software delivery is exposing a growing fault line for banks and financial services firms. The risk that speed is outpacing testing discipline, with direct consequences for resilience, customer trust and regulatory exposure.
Across the industry, engineering teams are being pushed to deliver faster than ever, driven by competitive pressure, digital transformation programmes and the rapid adoption of AI-assisted development. Release cycles are shortening, deployment pipelines are expanding and expectations around time-to-market continue to rise.
Yet beneath these gains, many institutions are grappling with a quieter but more systemic issue: testing frameworks and quality assurance practices are struggling to keep pace with the scale and complexity of modern software delivery.
The result is an increasing risk that defects escape into production, often only becoming visible when they disrupt critical customer journeys or core banking services.
Khurram Javed Mir, founder of Kualitatem and Kualitee, warns that what often appears to be rapid progress can mask deeper structural weaknesses in software quality practices.
“Imagine your Series B startup, flush with private equity cash, pushing 50 engineers to deliver MVPs weekly,” Mir started. “Metrics soar. Your deployment speed goes up by 300%, and velocity dashboards turn green, until a sneaky regression in your checkout flow tanks the app during Black Friday peak.”
For financial institutions operating complex, interconnected systems, that scenario is more than hypothetical. A single defect in a payments flow or authentication layer can quickly escalate into a major incident.
“If this sounds familiar, this often isn’t a sign of bad luck. In my experience, hypergrowth often masks a bigger crisis: skimping on testing discipline,” Mir sad.
The ‘speed trap’
The issue is not typically visible in early-stage environments. Smaller teams benefit from shared context and tighter feedback loops, allowing quality to remain intact even as delivery speeds increase.
“From what I’ve seen, early-stage teams rarely struggle with quality,” Mir remarked. “Small engineering groups share context and communicate constantly.”
However, as organisations scale, something increasingly relevant for banks modernising legacy estates or launching digital platforms, that balance begins to break down.
“The problem for software development, in particular, is that code ownership fragments and multiple teams ship in parallel,” Mir wrote in a recent Forbes Council analysis. “Development capacity expands faster than the testing discipline.”
Initially, delivery metrics continue to improve, creating a false sense of security. But beneath the surface, defect escape rates begin to rise.
“At first, the impact is invisible, as delivery metrics can still improve,” Mir noted. “Over time, however, defect escape rates rise until a visible incident forces leadership attention.”
‘Quality debt’
Mir outlined a familiar trajectory that many large organisations, particularly those undergoing digital transformation, will recognise.
“Over the past decade, I have seen this issue impact many venture-backed and private equity portfolios, especially when growth compresses development timelines,” he said.
The pattern unfolds in stages, starting with rapid, seemingly low-risk delivery, followed by breakdowns in feedback loops as systems scale, and ultimately culminating in what he describes as ‘quality debt’ becoming a drag on the business.
“Engineering time shifts toward incident response and stabilization,” Mir explained. “As the confidence in deployment pipelines declines, teams slow down because they no longer trust releases.”
For banks, this erosion of trust in deployment pipelines can have regulatory implications, particularly under frameworks such as DORA and operational resilience regimes, where firms must demonstrate control over change and system stability.
“The sad truth is that leadership often only recognizes the problem after customers experience the consequences,” he added.
“In my experience, hypergrowth often masks a bigger crisis: skimping on testing discipline.”
– Khurram Javed Mir
A key takeaway for QA and testing teams is that scaling quality cannot be solved purely through additional tooling or headcount.
“Organisations rarely recover from quality breakdowns by buying new tools or expanding QA teams alone,” Mir stressed. “The organisations I have seen regain control after quality-related setbacks made structural decisions.”
One of the most critical shifts is redefining ownership of quality. “When no team explicitly owns production stability or defect escape rates, issues become inevitable,” he said.
For financial services firms, this aligns with a broader industry shift towards embedding QA into engineering squads and making reliability a shared responsibility across development, testing and operations.
Mir also cautioned against treating automation coverage as a headline metric.
“Automation coverage is often treated as a vanity metric,” he said. “Teams attempt to automate everything, producing suites that become slow and brittle.”
Instead, he advocates a more targeted approach that will resonate strongly with banking QA teams prioritising critical systems.
“Another way to approach this is to treat automation as risk management,” Mir continued, highlighting the need to prioritise high-impact areas such as “payment systems, authentication flows, compliance processes and service integrations.”
This risk-based testing strategy is particularly relevant in financial services, where failures in these domains can trigger not just customer disruption but regulatory scrutiny.
Enforcing quality through pipelines
Perhaps most critically, Mir emphasised that testing standards must be enforced through delivery pipelines rather than relying on process alone.
“Testing standards rarely survive delivery pressure unless they are enforced through systems,” he stressed.
By embedding controls directly into CI/CD pipelines, organisations can ensure that quality gates are not bypassed in the pursuit of speed, “so code cannot progress if critical checks fail,” he wrote.
For banks, this approach aligns with the growing emphasis on automated controls, auditability and traceability in software delivery processes.
Mir also pointed to the widely adopted DevOps Research and Assessment (DORA) metrics as a practical framework for balancing speed and stability.
“These metrics measure both speed and reliability,” he said, referencing deployment frequency, lead time for changes, change failure rate and mean time to restore service.
For QA teams, this reinforces the need to move beyond productivity metrics and focus on indicators that reveal systemic risk. “Many organisations track productivity metrics while ignoring stability indicators,” he warned.
Ultimately, the message for financial services firms is clear: testing discipline is no longer a background engineering concern but a core component of business resilience.
“Speed without discipline almost always introduces operational risk that eventually becomes a business constraint,” Mir concluded.
He finished by warning that organisations must rethink how they position QA internally. “They will have the most success in preventing this issue if they see testing discipline as a capital protection strategy rather than an engineering preference.”
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