Mortgage Technology Platform
Case Study
A UK mortgage technology provider supporting brokers and lenders with digital applications needed a QA solution to stabilize complex FMA and DIP journeys
85%
Reduction in manual data setup
60%
Decrease in flaky test failures
3x
Faster regression cycles in CI/CD
Challenge
Mortgage application journeys such as Halifax FMA and DIP involve strict validation rules, conditional fields hidden behind feature flags, and integrations with multiple lender systems. QA teams were slowed by brittle UI steps, inconsistent fixture data, and recurring issues in lender mappings. This created high risk during releases and limited visibility into failures.
Solution
We designed an API-first automation framework to bring determinism and reliability into mortgage testing:
- API-Driven Setup: Used Playwright’s request context to set up fixture records, create applicants, and validate backend state before touching the UI.
- Deterministic Data Fixtures: Environment-aware test data to ensure repeatable flows for DIP, FMA, Quote, Status, and EPC APIs.
- Resilient UI Automation: Modular TypeScript page objects with recovery logic, defensive waits, and selectors tolerant to feature-flagged changes.
- CI/CD Integration: Azure DevOps pipelines with parallel workers, HTML reports, videos, and network traces for transparent triage.
- Performance Smoke Tests: Lightweight k6 checks on critical endpoints to catch latency regressions early.
Before Automation
✘ Manual setup required for every FMA/DIP test run
✘ Flaky UI scripts broke with feature-flag changes
✘ Limited visibility into lender mapping errors
✘ Regression cycles slow and resource heavy
After Automation
✔️ API-driven setup cut data preparation time by 85%
✔️ Flaky test failures reduced by 60% through backend-first checks
✔️ Regression cycles ran 3x faster with parallelized pipelines
✔️ Lender mapping flagged earlier through network-level assertions
Results
The automation framework transformed high-risk mortgage journeys into stable, repeatable flows. By replacing manual setup with API-driven data creation, the team eliminated hours of repetitive preparation and reduced flaky failures caused by inconsistent test states. Network-level assertions flagged lender mapping and validation issues earlier, making defects easier to trace and resolve. With regression runs now supported by detailed logs, videos, and traces, QA and development teams gained far greater confidence in test outcomes.
Beyond efficiency, the project delivered real business impact. Regression cycles accelerated in CI/CD, giving stakeholders faster, more reliable feedback on release readiness. Lightweight performance smokes added visibility into the health of critical APIs, ensuring new features shipped without introducing hidden risks. Most importantly, the platform gained a scalable QA foundation that safeguarded complex FMA and DIP journeys while supporting quicker, more confident product delivery.
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