As digital-only banks scale rapidly, maintaining release velocity without compromising quality becomes critical—especially in mobile-first, cloud-native environments. A leading UK-based app-only bank set out to strengthen its quality engineering practices to support daily deployments across multiple devices, operating systems, and environments.
The objective was to move away from manual, time-intensive testing toward a scalable, automation-first model. By embedding automation into the development lifecycle and improving environment stability, the organization enabled faster releases, improved reliability, and consistent customer experiences across channels.
Transformation Timeline
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The Challenge
The bank needed to significantly increase release frequency while managing daily deployments across multiple devices, operating systems, and environments. This required a scalable and reliable testing approach that could keep pace with rapid development cycles.
Manual regression testing created long execution cycles, slowing delivery and making it difficult to meet release timelines. Defects were often identified late in the lifecycle, leading to rework and increased risk before production releases.
Environment instability further disrupted execution, causing delays and inconsistent test outcomes. Without transformation, these challenges limited release velocity, increased operational effort, and impacted production readiness.
Our Approach
Delivered an automation-first QE transformation that accelerated releases, improved defect detection, and ensured stable execution across environments.
Defined Automation-First Strategy
Implemented a unified automation approach anchored in Automation First and In-Sprint Automation principles to enable continuous testing.
Built Reusable Automation Framework
Developed a BDD-based automation framework to improve reusability, maintainability, and support frequent release cycles.
Enabled CI/CD Integration
Integrated automated regression suites with Jenkins to enable overnight execution and parallel testing across environments.
Improved Early Defect Detection
Shifted testing left by enabling component-level validation and proactive test data setup to identify defects earlier in the lifecycle.
Stabilized Test Environments
Introduced overnight environment health checks and dedicated on-premise test labs to improve stability and execution reliability.
Partner & Technology Ecosystem
The engagement was delivered using Coforge’s AI-led engineering capabilities and cloud-native delivery framework.
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Impact to Date
6x Faster Releases
80% Defects Detected Early
4x Effort Savings
<0.17% Defect Leakage
Business Impact
- Increased release frequency while maintaining high quality
- Reduced regression cycle time and manual effort
- Improved production readiness through early defect detection
- Enhanced stability across multi-device and multi-OS environments
- Improved consistency of customer experience across channels
- Strengthened visibility and governance through centralized reporting
Looking Ahead
With a scalable automation framework and continuous testing model in place, the bank is well-positioned to further accelerate digital innovation, enhance customer experiences, and sustain high-quality releases at scale.
Value Delivered
- Automation-Led QE Foundation: Established a scalable automation-first QE model designed for high-frequency digital banking releases.
- Accelerated Release Velocity: Enabled faster, more reliable deployments through CI/CD-integrated automation.
- Improved Quality & Stability: Reduced defect leakage and enhanced system stability across devices and environments.
- Operational Efficiency Gains: Delivered significant effort savings and improved execution efficiency through automation.
