Case Study · Banking
AI Agents with Playwright Test Automation for Banking Cash Management Operations
A five-stage pipeline pairing AI agents with Playwright test automation, built to move a leading Indian private-sector bank's Cash Management Module from full-suite manual regression to impact-based testing.
Regression cycle Target
- 1,200+Manual regression cases in SIT today
- 8+CMM sub-modules in scope
- 78 to 92%Target coverage per sub-module
01 / 05
Client Snapshot
The client is a leading private-sector bank in India. Its Cash Management Module (CMM) underpins how corporate banking customers move and reconcile money, and it sits directly on top of real fund movement. It has to work correctly every time.
Qualitrix is applying an AI agent and Playwright test automation pipeline to change how this module is regression tested. The engagement is in progress, so the figures on this page are targets rather than measured results.
- Corporate Collections
- Vendor Payments
- Bulk Disbursements
- Liquidity Sweep & Pooling
- Virtual Account Management
- Host-to-Host Integration
- MIS & Reporting
- API Banking Gateway
02 / 05
The Challenge
The CMM had grown into a complex system, and its manual regression process had grown with it. That created a structural bottleneck, not an occasional slowdown.
- A large, interconnected system. The module spans 8 or more sub-modules, each with its own logic, data flows and downstream dependencies. The manual regression suite had passed 1,200 test cases in system integration testing. That is a sign of a mature QA discipline, and also a measure of what every release had to clear.
- The same checks for every change. Each release ran a fixed, largely static list of manual checks. A one-line fix to liquidity sweep logic meant working through as much of the suite as a large change to collections or disbursements, because a manual process cannot tell in advance which sub-modules a change touches.
- Release speed set by the script list. Releases moved as fast as the QA team could work through the checks, not as fast as a change was ready to ship.
- A high cost for any missed case. A collections exception that slips through, a vendor payment that mishandles a return, or a host-to-host exchange that fails to recover from an error becomes a reconciliation break, a delayed settlement or a compliance gap. Regulators and corporate customers both expect this system to work without exception.
- Testing that scaled with headcount. As the module kept getting more complex, manual regression could only keep up by adding people. The bank needed testing that scaled with the system instead.
Every release today
Regression sits on the critical path to every release
03 / 05
The Approach
Qualitrix is applying a five-stage pipeline that pairs AI agents with a Playwright test automation layer. Each stage narrows what the next one has to do, so testing effort follows the change instead of the size of the suite.
- 01Repository Intelligence
- 02Context Engineering
- 03Test Generation
- 04Impact Analysis
- 05Test Execution
Repository Intelligence
AI maps the CMM's code, APIs and connectors into a knowledge graph. Instead of treating the module as a black box that only shows its behavior through the UI, the pipeline learns how the 8+ sub-modules and their integrations relate to each other. Every later stage builds on this map.
Knowledge graphContext Engineering
From the knowledge graph, the pipeline pulls only the context tied to a specific CMM change, not the whole codebase. A change to disbursement approval logic brings in disbursement context and nothing else. That is what keeps the later stages precise instead of noisy.
Change-specific contextTest Generation
Using that context, the pipeline generates BDD and Playwright test scripts directly from CMM stories, combining AI-native intelligence with Qualitrix's own frameworks. It is designed to replace one of the slowest parts of the current process, translating a business requirement document into test cases by hand.
BDD and Playwright scriptsImpact Analysis
Before any test runs, the pipeline flags exactly which CMM flows and components a change affects. This is the step that moves regression from running everything to running what matters, so each release tests the flows actually at risk.
Affected flows and componentsTest Execution
Tests run on Qualitrix's proprietary framework, built on open-source Playwright and integrated directly into the bank's CI/CD pipelines. Because there is no licensed commercial tool underneath, tooling costs and license limits don't hold execution back as coverage spreads across more of the CMM's sub-modules.
Runs inside CI/CDBuilt for Banking-Grade Complexity
A few design choices target what makes banking systems harder to automate than typical enterprise software.
- Built on open-source PlaywrightA proprietary framework on Playwright rather than a licensed commercial tool, with no licensing lock-in as coverage expands.
- AI-native intelligenceThe pipeline works out what a piece of CMM code does before it generates a test for it, rather than pattern-matching on the UI alone.
- Framework-agnosticIt integrates with the bank's existing CMM environment, with no migration to a new testing stack.
- BFSI-specific depthGrounded in real Cash Management Module QA delivery, not adapted from a generic automation template.
04 / 05
Targeted Impact
Each target ties back to a point where the manual process was under strain.
These are the coverage and cycle-time targets Qualitrix is working toward in this engagement, based on typical patterns from comparable Cash Management implementations. They describe the intended outcome of the pipeline above and have not yet been independently measured for this engagement.
- Regression from roughly 10 business days to 2. The bottleneck was never the tests themselves. It was rerunning all of them regardless of what changed, and impact-based selective execution removes that.
- Only the affected flows run. Impact analysis replaces the full-suite run on every release, so a one-line fix no longer carries the testing cost of a major change.
- Less script maintenance. Self-healing scripts are designed to auto-resolve most failures, so a routine interface change no longer forces a manual fix to the automation suite.
- Coverage that keeps pace. AI-generated scenarios from context engineering replace manual BRD-to-test-case mapping, so coverage can grow with 8+ evolving sub-modules instead of falling behind them.
Key outcomes at a glance
| Dimension | Manual today | Qualitrix target |
|---|---|---|
| Regression cycle time | Roughly 10 business days | Roughly 2 days, via impact-based selective execution |
| Test selection | Full suite run every time | Only the CMM flows a change actually affects |
| Script maintenance | Manual fix required per change | Self-healing, designed to auto-resolve most failures |
| Test scenario authoring | Manual BRD-to-test-case mapping | AI-generated scenarios from context engineering |
| Release readiness | Regression sits on the critical path | Regression no longer gates release timing |
Target automation coverage by sub-module
Future begins with trust.
Driving quality, enabling trust, powering mission-critical banking.
05 / 05
The Bigger Picture
Cash Management Modules do not fail quietly. Corporate customers depend on collections, disbursements and liquidity sweeps running correctly and on time. When a host-to-host integration or an API banking gateway behaves unpredictably under load or in an edge case, the result is a reconciliation problem or a compliance finding. As CMM functionality expands, testing either scales with it or becomes the reason releases slow down.
The shift this pipeline targets applies across regulated, high-stakes systems. Exhaustive manual testing of everything on every release gives way to impact-aware testing that runs what a change puts at risk. For a bank, that means shipping CMM improvements at a steady pace while keeping the coverage a mission-critical system needs.
If your cash management or corporate banking platform is still bound by full-suite manual regression, Qualitrix can help you build an AI agent and Playwright test automation pipeline designed for BFSI-grade systems. Get in touch with Qualitrix to talk through your quality engineering needs.