CASE STUDY

Advanced QA for Insurance Risk Management Portal

By adopting an agile QA approach with robust manual and automated testing, our QA team accelerated release cycles and enhanced platform stability, all while ensuring high quality across critical insurance workflows.

FINTECH

ABOUT THE CLIENT

Empowering Insurance with Smart Risk Solutions

Our client, a US-based innovator in insurance tech, offers a web portal for managing P&C and Workers’ Compensation claims. The platform supports risk assessment, compliance training, real-time alerts, and integrates with backend systems (IVOS → ODS3 → PCB). With critical modules like billing, user management, and reporting, it required a robust QA strategy to ensure reliability and data integrity.

HIGHLIGHTS

95 %

pre‑release defect detection rate

400 +

test cases automated

Regression window cut from 5 days → 4 hours
Zero critical outages across three releases
Root‑cause analysis time reduced by 30 %

Tools we Used

PROBLEM STATEMENT

Slow Feedback Loops and QA Gaps in Agile Workflows

The client’s portal faced slow releases and recurring bugs due to limited QA coverage and delayed feedback. They aimed to enhance QA effectiveness and speed while covering diverse modules like claims, billing, and training. Key challenges included integrating QA into agile workflows, ensuring end-to-end stability, and balancing manual and automated testing to maintain quality without slowing delivery.

OUR SOLUTION

Enhancing Test Coverage with Automation and Real-Time QA

Developed a risk‑based test plan covering both customer and staff journeys.
Automated 420 regression and 75 smoke flows.
Generated compliant synthetic personas to widen data coverage.
Added visual checks for every responsive layout and dashboard.
Plugged the test grid into Azure DevOps for trigger‑on‑commit runs.

What we did?

Test Strategy & Planning

Automation Framework Implementation

Manual Testing & QA Support

Reporting, Feedback & CI/CD Integration

Test Strategy & Planning

We conducted workshops to map the lifecycle of claims and help-desk tickets, creating a traceability matrix linking user stories to test cases. Using synthetic data, we safely tested edge cases without compromising privacy. Power BI dashboards visualized coverage, defect trends, and business impact, helping product owners prioritize high-risk areas.

Automation Framework Implementation

We built a two-layer test engine using Selenium, Playwright, Cucumber, and Maven, with a self-healing locator library and visual testing via Applitools. Tests ran across 12 screen widths, and CI/CD in Azure DevOps cut execution time from 9 hours to 40 minutes. Intelligent retries and a custom stability score reduced test flakiness to under 2%.

Manual Testing & QA Support

We used session-based exploratory testing for complex flows like approvals and subrogation, revealing issues missed by scripted tests. WCAG-AA accessibility checks, localization reviews, and sanity tests on production-like data ensured release readiness. Defects were logged in Azure Boards with correlation tags, reducing triage time by 30%.

Reporting, Feedback & CI/CD Integration

We automated nightly Azure DevOps jobs to run browser and API tests in Kubernetes pods, with results visualized in Power BI heatmaps. HTML and JUnit reports were emailed to execs, and a Slack bot alerted engineers to critical failures. Builds meeting a 95% quality gate auto-shipped, reducing hot-fix deployments by 70% through continuous testing and real-time insights.

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