Why do most banks struggle to scale test automation beyond 33% coverage?

Many banks focus on automation tools but overlook the operating model required to scale them. Challenges such as fragmented frameworks, high maintenance effort, limited reusability, and lack of integration with delivery pipelines often prevent automation programs from moving beyond 33% coverage.

What is the most effective way to increase test automation coverage in banking?

Banks can improve automation coverage by adopting an industrialized Quality Engineering approach that combines embedded automation, reusable assets, AI-assisted maintenance, and pipeline-native execution. This helps organizations scale automation efficiently while reducing testing effort and release cycle times.

How does AI help improve test automation in banking?

AI supports test automation by reducing script maintenance, identifying impacted test cases, improving defect prediction, and accelerating test execution. When combined with Quality Engineering practices, AI enables banks to achieve higher automation coverage with greater efficiency and reliability.

What business benefits can banks expect from achieving 60%+ automation coverage?

Higher automation coverage helps banks accelerate release cycles, improve software quality, reduce testing costs, and increase engineering productivity. It also strengthens operational resilience and enables faster delivery of digital and AI-driven banking initiatives.

Why is Quality Engineering becoming critical for AI-first banking?

AI-first banking demands faster releases, higher system reliability, and continuous validation of digital experiences. Quality Engineering provides the foundation for scalable automation, governance, and testing practices that help banks innovate confidently while maintaining quality and compliance.

What framework can help banks scale test automation beyond 33% coverage?

Many banks struggle to move beyond 33% automation coverage despite significant investments in testing tools. The challenge often stems from fragmented automation practices, maintenance complexity, limited asset reusability, and disconnected delivery pipelines. Maveric Systems has identified five critical pillars that help organizations industrialize Quality Engineering, enabling higher automation coverage, improved release velocity, stronger software quality, and greater engineering productivity. Institutions that adopt a structured, engineering-led approach are better positioned to achieve sustainable automation at scale.

How to Scale Test Automation in Banking Beyond 60%

Test automation has been a strategic priority for banks for over a decade, yet average automation coverage remains stalled at just 33%. The challenge is no longer about tools – it is about rethinking the operating model behind automation.

Download our latest whitepaper that examines the structural barriers that prevent automation from scaling and presents a proven five-pillar framework for industrializing Quality Engineering across banking. From embedded automation and reusable assets to AI-enabled maintenance and pipeline-native execution, it outlines a practical path to achieving 60%+ automation coverage while improving release velocity, engineering productivity, and software quality.

Grounded in deep banking domain expertise and real-world delivery experience, the paper demonstrates how automation can evolve from a tactical testing function into a strategic engineering capability, enabling faster innovation, stronger operational resilience, and the confidence required to deliver AI-first banking at scale.