Challenge
From manual effort to automated success
This investment bank faced some big hurdles that slowed down new product releases and drove up costs and risks. The main problems included:
Heavy manual workloads: Regression testing ate up time and resources, stalling release speed
Spotty automation: Without standard frameworks and enough in-sprint adoption, automation couldn't be scaled
Knowledge gaps: Long training times and leaning too hard on a few experts created bottlenecks and capped growth
Lack of visibility: Manual reporting meant no real-time view of QA metrics, test coverage, or defect trends, making smart decisions tough
These issues hit time to market, productivity, and overall quality hard, leading to wasted effort and higher costs.
Solution
Driving efficiency with a unified framework
We put a multifaceted plan into action, focusing on people, processes, and technology to fix the bank's issues. Key moves included:
Building expert teams: We brought in over 180 skilled pros, matching them to specific business areas to own quality and speed up delivery
Launching a dedicated academy: We set up a training academy to get new team members up to speed fast and train them on advanced automation, gen AI, and cloud testing
Unifying automation frameworks: We standardized the bank's tools, moving from Selenium to faster technologies such as Playwright and Cypress. This included building reusable assets to speed up development across teams.
Integrating in-sprint automation: We put automation into sprint cycles with clear quality goals that supported continuous testing and reduced regression work
Using gen AI and intelligent automation: Working with the client, we brought in LLM-based tools like Microsoft GitHub Copilot to help improve test writing and maintenance
Establishing cloud-based observability: We used the cloud to scale test execution and build central dashboards, giving a real-time view of QA performance and boosting release stability
Impact
Faster, smarter quality engineering
This partnership changed the game for how the bank handles quality engineering. The results speak for themselves:
Up to 80% automation of repeatable regression testing, cutting manual work and test cycle times
A modern tech stack with a full move to Playwright and Cypress, boosting test reliability
Faster test development thanks to shared frameworks and reusable engineering assets
Quicker onboarding and scalability, as the academy and role-based certifications cut ramp-up time and the need to rely on a limited pool of experts
Release stability improved by over 35%, with better test coverage and visibility leading to fewer bugs after launch
A 30% productivity boost through standardized tools, better automation, and LLM-assisted workflows
The future of banking QA
By turning its QA function into a modern, nimble, data-driven operation, the bank has significantly improved time to market and raised the bar on quality. Our work has set the stage for ongoing innovation and efficiency.
Looking ahead, we're adding deeper domain knowledge and gen AI learning paths to the academy's curriculum. We're also working to make AI a bigger part of intelligent test generation. Our shared goal is to move toward a unified, cloud-native, and codeless ecosystem that helps our clients maintain a competitive edge in financial services.
