Insurance companies operate in a world where every release carries weight. Policy management system testing must be precise. Legacy system modernization QA must avoid disruptions. And risk-based testing for insurers must cover the most critical business flows without slowing delivery.
In this article, you will learn:
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High-performing insurance QA teams share a common pattern. They modernize in focused, measurable ways that create lasting impact. Our research from the State of Software Quality Report 2025 shows that:
These pillars work together to create an enterprise QA strategy that scales. Each plays a strategic role in scaling QA in insurance enterprises and enabling greater test automation at scale.
Scaling QA in insurance enterprises means working within a complex mix of processes, systems, and regulations. Each layer adds a unique challenge that impacts QA team structure in insurance and the ability to deliver consistent quality at speed.
Many insurance teams operate with varied approaches to daily work. Each underwriter, CSR, or back-office specialist follows personal habits rather than a single standardized process. This creates variation in execution and reduces visibility for leadership.
Legacy systems in the insurance application lifecycle can limit integration between platforms. When these systems do not communicate, teams fill the gaps with local spreadsheets or manual logs. This means QA efforts are focused on tracking issues rather than preventing them.
Process gaps are common in policy issuance, renewal prep, FNOL intake, and bordereaux management. Converting these into repeatable workflows ensures every QA cycle runs with accuracy. Once repeatable, these workflows support risk-based testing for insurers by focusing resources where they matter most.
Clear workflows enable faster test environment orchestration, smoother automation integration, and more reliable execution. The result is a QA ecosystem that is predictable and scalable.
Scaling QA in insurance enterprises requires a clear strategy that aligns tools, people, and processes. Insights from the State of Software Quality Report 2025 show that high-performing teams focus on AI-driven testing, modern development practices, and CI/CD adoption to achieve balance between speed and quality.
Hybrid testers adapt to shifting priorities. They work seamlessly with cross-functional QA collaboration, use CI/CD in insurance systems for rapid feedback, and apply AI-driven platforms to maintain quality. Their mix of automation skills and business understanding ensures QA roles in enterprise delivery contribute directly to project outcomes.
High software quality remains the top goal for 69% of teams. Many achieve it by embedding QA into DevOps workflows, combining continuous testing with AI-driven defect detection. This approach increases scalability, maintains compliance, and enhances customer satisfaction.
Cloud-based testing platforms, performance testing tools, and mobile testing tools expand coverage without increasing overhead. These investments improve scalability and ensure that insurers can deliver high-quality releases on time.
Saga is a specialist insurance provider serving the over-50s market in the UK. The company offers a wide range of tailored insurance products and financial services, with a mission to become the trusted superbrand for older customers. Delivering on this vision means ensuring seamless customer experiences through robust software quality practices.
To achieve this, Saga invested in best-in-class testing solutions as part of its digital transformation. The focus was on scaling QA in insurance enterprises in a way that maintained high quality while increasing speed and efficiency.
Saga adopted the Katalon quality management platform to optimize regression testing and API testing. The implementation and Guidewire integration were completed in-house with support from Katalon’s team. According to Johnson, “The transition to Katalon was very smooth, and it is a tremendously easy testing platform to pick up.”
With Katalon, the team could test system functionality across critical front-end journeys and validate integrations with third-party quote aggregators. Features like object recognition, manual view scripting, and low-code authoring made it accessible to a broader set of testers, supporting cross-functional QA collaboration and faster scaling test coverage.
Katalon enabled Saga to significantly increase operational efficiency. The QA process now covers at least 100 scenarios per product per development release, compared to around 30 scenarios before automation. Testing time for regression dropped from two days to two hours, aligning with CI/CD in insurance systems for faster releases.
The team reduced overtime, lowered associated costs, and empowered more testers to contribute, thanks to the platform’s versatility. Beginners could create automated tests without coding, while advanced users could extend functionality through custom keywords
📚 Read the full case study: How Saga eased into Agile software testing and increased capacity threefold
A global provider of digital solutions for the life insurance and financial services industries serves over 100 insurance carriers, more than 2,500 distribution partners, and connects with over 500,000 agents and advisors worldwide. As its platform grew more complex, manual-heavy testing could no longer keep pace with the business.
To modernize its quality engineering practice, the company worked with implementation partner KMS Technology to shift from manual, siloed testing to an automation-first, AI-driven model embedded across the software development lifecycle.
The team rebuilt its testing practice in stages. First, they moved from manual testing through a hybrid phase into a fully automation-first model built on Katalon Studio, automating more than 2,500 critical test cases and building regression suites designed to support continuous delivery.
From there, AI-driven capabilities were layered on top to analyze real-world user interactions and prioritize high-value test scenarios, shifting testing from purely functional validation toward genuine business assurance. Quality engineering, DevOps, and product teams were then aligned around a single system of record using Test Management, with automation and reporting embedded directly into the CI/CD pipeline.
The transformation cut testing cycle time by up to 75%, taking release timelines that once took weeks down to hours. Automation coverage reached 90 to 95%, letting the team validate complex workflows at scale without adding headcount, while production defect leakage dropped by 60%.
Beyond the numbers, quality engineering became a shared responsibility across QA, development, and product rather than a release-day checkpoint, giving both engineering teams and leadership real-time visibility into release readiness.
📚 Read the full case study: How an Insurtech Leader Achieved 75% Faster Releases with AI-Driven Quality Engineering