The financial services industry is evolving at pace; driven by new digital-first experiences that are personalized, always-on, and laser focused on customer experience. In order to remain competitive in such an environment, businesses, whether they are incumbents, fintechs, credit unions or insurers, must develop and implement a robust and forward-looking digital transformation strategy.

But not all transformations are not created equal.

Focused exclusively on financial services, Extractable, a Silicon Valley-based digital consultancy, takes a unique approach to digital transformation. Following the Design Thinking model coined and championed by the Hasso-Plattner Institute of Design at Stanford (d.school), Extractable tackles transformation from the ground up — with the user. The group works closely with clients in banking, investment management and insurance to deeply understand their customer’s unique needs. They leverage primary customer and market research in tandem with quantitative data analysis to define the actual usage and performance of digital systems. Additionally, they work closely with management teams to align departmental business goals to a digital strategy, placing equal emphasis on cultural transformation.

“Our work is really around applying our proprietary research, knowledge and experience to ensure that those companies we work with get the most out of their digital channels,” notes Craig McLaughlin, Extractable’s CEO. “For us, customer experience and user centricity are in our DNA, having served clients in Silicon Valley for over 20 years. We seek to extend that expertise, looking to bring together first-person user research in terms of who the company’s customers are, and marry that squarely to the overarching business goals and, holistically, what each department is looking to achieve. Finally, we bring that together in the form of a cohesive digital strategy,” McLaughlin explains.

“A lot of the time, what ‘digital’ is trying to do is not necessarily the same as what the organization is looking to do more broadly,” he continues. “So, we typically spend a lot of time with CEOs and others in the business to understand what the traditional strategy looks like, and then develop ways in which we can manifest that into the digital realm. It’s about bringing all the disparate teams and departments together under one strategy that is absolutely cohesive and unified, and laser-focused on a single goal — This is particularly true for financial institutions such as credit unions.”

The need for such a strategy, says McLaughlin, is being driven by the broader trend in the sector that sees customers shift from using brick-and-mortar financial services, such as bank branches, to their digital counterparts. This has been accelerated by the impact of the global COVID-19 pandemic, he explains. “People have been shifting away from visiting branches for some time, but we’re now seeing that they just can’t do that even if they want to. So, for companies in the industry, whatever their learning curve was around switching to a digital-first strategy, that’s being pushed and accelerated. A lot of us, as a result of COVID, will adapt to and adopt the digital world, and that really changes things for these organizations.

“With that in mind, I couldn’t imagine being a CEO of a bank today and saying, ‘our strategy hinges on the building of branches’,” says McLaughlin. “Really, they should blend the digital and physical and deeply consider how that combination enables them to stand for something special. The big question to answer is, how can an incumbent FSI manifest their unique brand story into a set of digital and physical experiences that can be delivered in concert — at scale.”

Extractable’s customers include many of the largest credit unions in the United States. Extractable is currently working with Visions Federal Credit Union, a New York-based CU that offers a host of services such as savings, checking, loans, mortgages and investments, and online banking. Visions FCU is also working with Extractable to launch a member-focused digital transformation. “It’s a fantastic credit union with a very clear vision in terms of growth,” says McLaughlin. “One of the great things about the company is its culture and leadership, which is the single most critical success factor when you’re driving change.

“Where we come into that work is carrying out research into the organization’s members and prospective members, with a view towards developing a user-centered digital strategy,” he adds. “Every credit union we work with has something unique, and Visions has a great opportunity for growth. We’re particularly impressed with the organization’s ability to prioritize and focus on collaboration and using data insights to guide decision making.”

The transformation at Visions is still in its early stages, with Extractable focused on the discovery and research work that informs the digital strategy. This, shares McLaughlin, includes developing an understanding of each department’s goals, and the specific areas of focus for growth that can be improved with digital.

“It’s really all about establishing a plan for how the client can delight the existing member and win the prospective member — digitally,” he says. “With that roadmap in place and alignment with the credit union leadership team, the focus shifts to how those goals can be achieved through the digital experience.”

Below are eight practical QA strategies engineering and DevOps teams should consider as their organisations navigate digital change.

1. Put Consumer Experience and Brand at the Centre of QA

Delivering a seamless user experience across mobile, web, tablet, and emerging form factors such as smartwatches is the primary measure of a successful digital product. QA must be designed with the end user in mind, not just functional correctness. Practical test types that support this goal include:

• Crowdsourced testing — gathers realistic, real-world feedback from users in diverse geographic locations, on genuine devices and network conditions.
• Payment gateway testing — validates end-to-end transaction flows, error handling, and security controls for e-commerce journeys.
• UI and functional testing — confirms that core workflows behave correctly and intuitively across browsers, operating systems, and screen sizes.
• Database validation testing — verifies data integrity and stability as the application scales.
• API testing — exercises integrations under varying network conditions, payload sizes, and bandwidth constraints to expose brittle contracts early.
• Cross-platform compatibility testing — ensures consistent behaviour across the full matrix of OS versions, browsers, and device types your users actually run.

Beyond defect detection, user-experience QA should answer three business questions: How easy is the app to access and use? Does the experience reinforce or damage brand reputation? Where can response times be improved? Automating as much of this coverage as possible frees QA engineers to focus on exploratory and edge-case testing where human judgement adds most value.

2. Shift to Full-Lifecycle Test Automation

Traditional QA was largely confined to functional and regression testing at the end of a project. The complexity introduced by IoT devices, microservices architectures, and multi-channel delivery makes that approach untenable. Test automation must now span the entire application lifecycle—from unit tests written alongside production code, through integration and contract tests, to end-to-end regression suites executed on every build.

A growing number of teams are adopting codeless or low-code test automation platforms, particularly for regression suites that must run frequently inside CI/CD pipelines. These tools can lower the barrier to entry for non-developers participating in QA, reduce script maintenance overhead, and improve the overall return on the automation investment. That said, codeless tools have limits—complex business logic and dynamic UIs often still require scripted approaches using frameworks such as Selenium, Playwright, or Cypress.

3. Embed Agile Testing Practices Across the Whole Team

In traditional waterfall projects, testing was a discrete phase near the end of delivery, creating a bottleneck and limiting time for meaningful quality work. Agile testing removes that bottleneck by making quality everyone’s responsibility throughout each sprint.

Key characteristics of mature agile testing include:

• Testers participating in story refinement and acceptance criteria definition, not just test execution.
• Developers writing automated unit and integration tests as part of the definition of done.
• Short feedback loops so defects are caught and fixed within the same sprint they are introduced.
• Continuous collaboration between business stakeholders, developers, and QA engineers to align on expected behaviour.

Faster feedback directly supports faster time-to-market—which, in a competitive digital landscape, is a meaningful business advantage.

4. Adopt DevOps and Continuous Delivery

DevOps breaks down the silos between development, QA, and operations teams, creating a shared pipeline where code moves from commit to production through a consistent, automated set of quality gates. For QA, this means:

• Automated test suites integrated directly into the CI/CD pipeline, blocking deployments when quality gates fail.
• Test environments that can be provisioned and torn down on demand, preventing environment drift from masking defects.
• Defect tracking and test management tools integrated with the pipeline so that reports, screenshots, and logs are available immediately after a run.
• Shared dashboards that give the whole team visibility into quality trends over time, not just pass/fail counts on the latest build.

Continuous delivery is the practical outcome: small, tested increments of software are always in a deployable state, reducing the risk associated with large, infrequent releases.

5. Use Advanced Analytics to Inform Quality Decisions

Traditional QA relied heavily on structured defect reports and planned test cycles. Digital products generate a much richer signal. Production telemetry, crash reports, performance metrics, app store reviews, and even social media sentiment can all surface quality issues that scripted tests miss.

QA teams that invest in analytics tooling can:

• Identify the features and user flows that generate the most errors in production and prioritise test coverage accordingly.
• Detect performance regressions by comparing run-time metrics across releases rather than relying solely on synthetic load tests.
• Mine user feedback from app stores and social platforms to find categories of defects that internal testing did not catch.

Feeding this data back into sprint planning closes the loop between production behaviour and the test strategy for the next release.

6. Establish a Testing Centre of Excellence (TCoE)

A Testing Centre of Excellence is a dedicated function that defines standards, tools, processes, and best practices for QA across an enterprise. Rather than every team reinventing its own testing approach, a TCoE provides shared frameworks that raise quality and reduce duplication of effort.

A practical model is to structure the TCoE in two layers:

• A smaller specialist group focused on non-functional testing disciplines—performance, security, accessibility, and compatibility—that require deep expertise and expensive tooling not practical to duplicate across every product team.
• A larger enablement group responsible for maintaining shared frameworks, automation libraries, onboarding guidance, and quality metrics that all delivery teams can consume.

As digital transformation scales across business units, a TCoE prevents quality from becoming inconsistent or fragmented.

7. Prioritise Mobile Testing

Mobile applications are frequently the primary interface between a business and its customers—used for everything from payments and bookings to location services and account management. Mobile release cycles are often faster than web cycles, which puts pressure on testing teams to deliver confidence quickly.

Cost-effective mobile QA strategies include:

• Automating regression tests to reduce the time required to validate each release on the most critical device and OS combinations.
• Using cloud-based device farms to access a broad matrix of real devices without the capital cost of maintaining a physical lab.
• Involving small TCoE specialist teams to run non-functional tests—performance under poor connectivity, battery consumption, and security—that product teams may lack the tooling to run themselves.
• Validating the complete end-to-end user journey rather than testing screens in isolation, since mobile failures often occur at integration points.

8. Apply Rigorous Quality Practices to Big Data Pipelines

Digital transformation depends on data. Customer behaviour analytics, personalisation engines, and operational dashboards are only as valuable as the data feeding them. If the data is incomplete, duplicated, or corrupted, business decisions based on it will be flawed.

Big Data QA addresses this by validating:

• Data integrity — records are complete, correctly formatted, and consistent across systems.
• Data privacy and security — personally identifiable information is handled correctly at every stage of ingestion, transformation, and storage, in line with applicable regulations.
• Pipeline performance — processing jobs complete within acceptable time windows under realistic data volumes.
• Data lineage — the origin and transformation history of key data sets can be audited.

Open-source frameworks built around platforms such as Apache Hadoop and Apache Spark can form the foundation of a Big Data QA framework. Commercial data observability tools are also available for teams that need faster time-to-value.

Conclusion

Digital transformation raises the stakes for software quality at every level—user experience, security, data integrity, and release velocity all matter simultaneously. QA teams that evolve alongside the technologies they test, embracing automation, DevOps collaboration, advanced analytics, and specialised disciplines like mobile and Big Data testing, become genuine accelerators of transformation rather than bottlenecks to it.

The eight strategies above are not sequential steps but complementary practices. The further along the maturity curve a team moves with each one, the more resilient its digital products become.