How a Brand in the Fintech Industry Accelerated Testing with KaneAI: From Scripts to Intelligent Automation

About

This is a Fintech brand specializing in digital wallet services, supporting over 100,000 merchants and processing millions of transactions daily. Its platform integrates with payment gateways, banking APIs, and fraud systems to deliver secure, real-time transaction processing across multiple geographies.

Like most Fintech providers, the company faced increasingly complex testing demands spanning payment workflows, regulatory compliance, API integrations, multi-device support, and rapid product iteration.

Lack of Coverage and Slow Progress Due to Hard-Coded Scripts

The company's testing infrastructure relied heavily on Selenium scripts maintained across multiple frameworks. While initially fast, this approach created problems that slowed them down:

• Script Maintenance Became a Burden: Each UI change or API update required manual script updates. Payment flow modifications meant touching dozens of dependent test cases. The team spent nearly 40% of their time fixing broken tests instead of creating new coverage. When the payment gateway interface changed, multiple scripts failed simultaneously, creating cascading delays in release cycles.
• Limited Coverage for Edge Cases: Scripted tests excelled at happy-path scenarios but struggled with regulatory compliance testing, KYC workflows, and fraud detection edge cases. These complex scenarios required rigid conditional logic that was expensive to implement and difficult to modify. The team couldn't test enough variations of transaction amounts, user geographies, and payment methods within their sprint cycles.
• Bottleneck in Test Creation: Writing new automation tests required SQL knowledge, Selenium expertise, and deep familiarity with their custom test framework. Business analysts and junior QA engineers couldn't contribute directly. New features took weeks to get full automation coverage. Compliance requirements sometimes went untested because test scenarios were too complex to script quickly.
• Scaling Testing Across Payment Types: As the company expanded into new payment methods and digital wallets, its test suite grew exponentially. Managing test data, maintaining environment setup, and orchestrating parallel execution across different payment gateways became increasingly complex.

Addressing the Challenge: Natural Language Intelligence

During a review of testing platforms, the company evaluated LambdaTest’s KaneAI for its core premise: write automated tests in plain English instead of code.

A small pilot in their payment processing module demonstrated dramatic improvements. Within weeks, KaneAI was rolled out across major parts of the QA workflow.

1. Natural Language Test Creation Transformed Speed

The first breakthrough came from replacing scripts with natural-language instructions. Instead of coding Selenium tests, QA engineers wrote scenarios like:

"Login as merchant user ‘test_merchant_1’, navigate to the wallet section, add a new payment method using a Visa card ending in 4242, and verify it appears in saved methods."

KaneAI converted this description into executable test automation across multiple frameworks simultaneously. Selenium Java, Playwright JavaScript, and Cypress implementations were generated from a single description. The test creation time had already dropped from 30 minutes to ~10 minutes here, including any prompt experiments.

More importantly, business stakeholders and junior QA team members could write basic test scenarios without coding expertise. This democratization of test creation meant the team could scale coverage without expanding headcount.

2. Self-Healing Tests Eliminated the Maintenance Drain

The second transformative capability was KaneAI’s test self-healing. Because tests were defined by intent instead of hard-coded selectors, the platform automatically adapted when interfaces changed. When the company's payment form CSS classes shifted during a UI redesign, KaneAI tests continued running without manual intervention.

Previously, this same change would have required the team to spend 2-3 days identifying affected tests, updating locators, and validating fixes. With KaneAI, there was no reaction needed. The tests healed themselves.

This capability freed the team from the test maintenance treadmill. They shifted time investment from "keeping tests alive" to "building new coverage."

3. Compliance and API Testing Became Accessible

Scenarios that were once too difficult to automate became straightforward. For instance:

"If transaction amount exceeds $5,000, verify that the system prompts for additional identity verification and logs the event in the compliance trail."

KaneAI:

• Interprets the business rule
• Generates the multi-step workflow
• Creates necessary test data
• Validates logs and audit trails

Previously, this required tangled conditional logic and long scripts. Now it took a single natural-language command.

Why KaneAI Became Indispensable

The company selected KaneAI because it solved the exact problems strangling their QA organization. But the platform delivered benefits beyond the initial proposal:

• Faster Onboarding for New Team Members: New QA hires could write executable tests on day two using natural language. Previously, they required two weeks of training on Selenium, their custom framework, and test infrastructure. Time to productivity dropped from 14 days to 2 days.
• Better Cross-Functional Collaboration: Business analysts, product managers, and QA engineers could now collaborate directly on test scenarios. A product manager could describe a checkout flow, KaneAI would generate tests, and the team could validate business requirements through automated execution.
• Reduced Testing Fragmentation: Previously, payment testing happened across three different frameworks and tools. KaneAI unified this through a single interface. Tests written once ran everywhere - Selenium, Playwright, Cypress, API tests, and mobile tests from a single definition.
• Intelligent Test Suggestions: As the platform learned the company's payment workflows, it suggested missing test scenarios. When a new payment method was launched, KaneAI proactively recommended edge case scenarios that human testers might have missed.
• Compliance-Ready Documentation: Test descriptions in plain English served as living compliance documentation. When regulators asked "How do you validate PCI compliance?", the company could show the exact test scenario validating encryption and secure payment data handling.

The Impact

After six months of KaneAI deployment across the payment processing module, the company measured these results:

• 3x faster test creation: New test scenarios dropped from 30 minutes to ~10 minutes. Monthly test creation velocity increased from 40 new tests to 240 new tests.
• 65% reduction in test maintenance: Manual script updates dropped from 85 hours monthly to 30 hours monthly. The team recovered approximately 660 hours annually, equivalent to a full-time engineer's output.
• 100% test coverage expansion: Payment workflow coverage jumped from 150 test cases to 300 test cases. Device coverage expanded from the 15 in-house devices to 10,000+ real devices available on the LambdaTest platform.

Now that LambdaTest has helped stabilize our payment testing and increased our coverage, our next step is to extend it to fraud workflows and new wallet products. We’re on track to shipping faster than we’d previously anticipated.

The combination of natural language test creation, self-healing intelligence, and multi-framework support addressed the company's core pain points. Their team now spends time on strategy rather than maintenance. Their coverage expanded without expanding headcount. Their release cycles accelerated.

For payment processing platforms, especially, KaneAI transforms testing from a bottleneck into a competitive advantage.

Book a demo to see how KaneAI can accelerate your payment platform's testing.