Client Background

The client is a global provider of a modular, AI-enabled banking platform that empowers financial institutions to unify banking operations, data, and customer journeys. Their platform supports multiple lines of business — from retail and SME to commercial and wealth — and integrates across channels (web, mobile, branch, contact centre). They position themselves as a long-term partner in digital transformation, helping banks move beyond legacy siloes to deliver personalised, scalable, and future-ready banking experiences.

Challenge

The digital banking enablement platform provider faced mounting challenges across its QA landscape. One of its Engineering Directors with responsibility for products driving over 60% of company revenue, recognised that multiple business units operated independently with limited testing cohesion. This isolation often led to inconsistent quality, integration failures, and costly release delays.

Despite multiple automation attempts, test coverage remained unreliable—over 90% of the 1,600 automated test cases frequently failed validation, forcing teams to revert to manual testing. The situation eroded confidence in QA outputs, increased regression times, and hindered feature releases. The lack of traceability and structured governance further prevented scaling automation sustainably which further contributed to low customer satisfaction.

Solution

Inspired Testing was engaged to restore confidence, reduce tech debt and create a mature, scalable QA capability. The engagement focussed on the following key areas:

• An initial phase established the best use of VeloAI (Inspired Testing's proprietary AI platform - a testing co-pilot designed to assist testers) to automate targeted test scenarios across iOS, Android, and Web. Metrics such as time to script creation and execution efficiency were tracked to quantify value and ROI.
• Reverse engineering of missing System Tests and Acceptance Test scenarios using BDD/Gherkin, established a structured and traceable repository for automation. The reverse engineering was achieved with the use of AI (ChatGPT). The process included the collection of all the relevant data first from numerous different sources. Once all the data was collected, it was entered into ChatGPT, and one comprehensive requirements document was created which was then used to create the BDD Feature Files.
• Automating high-value regression suites across web and mobile apps using robust frameworks and AI-assisted tooling, ensured reliability and maintainability. The above-mentioned Feature Files are then used with VeloAI to create integrated Test Scenarios into the clients existing code base. Benchmarking the company’s QA maturity, Inspired Testing developed a comprehensive governance framework and roadmap for sustainable quality evolution.
• Creating “squads” of experts with varying specialities to cover the full, end-to-end testing need (expected behaviour to manual test cases being created, to automation libraries being created, run and integrated) – for various different customer departments.
• Mentoring the company’s QA team through direct collaboration and training, as well as embedding AI in testing workflows to accelerate future automation and minimise maintenance.

All testing was executed remotely via secure, client-approved environments with strict data obfuscation and compliance standards.

Results Before Improvement

• Fragmented QA processes with unreliable automation
• Repeated automation failures eroding team confidence
• 20+ person-days per release in manual validation
• Lack of visibility and metrics across QA performance
• Minimal cross-team collaboration

Results After Improvement

• Unified, traceable QA framework built on BDD/Gherkin
• Reliable regression suite powered by AI-driven automation
• Initial 50+hours reduction in manual validation effort per team, per week increasing in each release
• Data-driven dashboards and performance metrics from VeloAI
• Continuous QA enablement across the company’s business units

Business Impact

• Enhanced Release Confidence: Increased trust in automated testing enabled faster, safer feature releases.
• Operational Efficiency: AI-driven automation and BDD reduced validation time and regression overhead.
• Sustainable Capability Growth: The company’s internal team gained hands-on skills and confidence in maintaining automation suites.
• Reduced Risk: Improved governance and traceability minimised defect escape rates and integration issues.
• Increased Confidence: With tech debt removed, our client is confident they are building on a solid foundation.
• Long-Term Partnership: This successful engagement has led to an extended collaboration focused on continuous QA maturity.

Why Inspired Testing

• Proven expertise in AI-assisted QA automation.
• Scalable delivery model combining consulting, automation, and capability upliftment.
• Dedicated leadership and certified consultants ensuring measurable outcomes.
• ISO 27001 and Cyber Essentials certified for quality and security compliance.