CASE STUDY

Scaling Fintech Test Automation Safely With AI

AI support at every testing stage, from test planning to verified automation, with QA engineers approving each step.

FINTECH

ABOUT THE PROJECT

AI Across the Testing Life Cycle

A fintech platform serving customers on web, mobile, and API layers released often, and manual QA could no longer keep pace. Its journeys move money and handle sensitive financial data, so tests had to be accurate and traceable. Codoid applied AI at each testing stage, from defining project rules and finding coverage gaps to generating and running automation. QA engineers approved every stage, and AI worked only with masked test data on approved environments.

HIGHLIGHTS

3X

Faster test case creation

60%

Reduction in script creation effort

AI gap analysis flagged missing test scenarios, and QA approved every suggestion before generation.
Every locator was confirmed on the live screen through Playwright MCP or Appium MCP before code was written.
AI ran only on test environments with masked data, and every change was logged for audit.

Tools we Used

PROBLEM STATEMENT

Fast Releases, Slow Automation

The client released new features often, and each release touched journeys where a missed defect could affect customer money or trust. Coverage gaps were hard to spot across so many flows. Hand-written scripts varied by author, and guessed locators broke when screens changed. Adding people would not close the gap fast enough. The team needed AI to take on repetitive work, with humans keeping control and customer data kept safe.

OUR SOLUTION

Scalable Test Automation Strategy

Test Planning: Documented workflows, rules, and standards.
Test Design: Identified coverage gaps using AI.
Test Creation: Generated standardized test cases with AI.
Automation: Verified UI elements using Playwright MCP and Appium MCP.
Execution: Ran end-to-end tests with pass/fail results.
Data Safety: Used masked test data and audit logs with QA oversight.

What we did?

Flow Blueprint

Gap Radar

Guided Build

Verified Journeys

Flow Blueprint

Captured the project’s rules, coding standards, and naming conventions in a Markdown instruction file before generation began. Every stage read from it, so feature files, page objects, and step definitions followed one convention across web, mobile, and API suites.

Gap Radar

Compared existing test cases against the requirements and flow document, then flagged flows with no coverage. The AI suggested new scenarios and updates to weak cases, and QA engineers accepted, edited, or rejected each suggestion before anything was generated.

Guided Build

Scaffolded the .feature file, step definitions, and page objects to match the project’s structure and Markdown rules. Playwright MCP and Appium MCP then confirmed every locator on the live screen, and the AI logged each change to a file.

Verified Journeys

Executed each automated scenario end to end on masked test data, confirming every journey behaved as a real user would expect. The result was a clear pass or fail, and a repeatable test ready for every future build.

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