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QualityKiosk’s Custom Python Solution Increases Release Velocity for a Global Fintech Leader

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Industry & Segment

Banking & Financial Service

Objective

To automate test case creation from product documentation using GenAI, reducing manual QA effort while improving test coverage, consistency, and release speed for a global fintech organization.

Overview

QualityKiosk partnered with a global fintech and payments company to implement a custom python platform for AI-driven test case generation that transforms product documentation into comprehensive test artifacts. This engagement addressed the critical challenge of manual test case authoring, which traditionally consumed significant resources and introduced quality inconsistencies.

Client Overview

TThe client is a global leader in financial technology and payment solutions, serving banking institutions, merchants, and commerce platforms worldwide. Its technology infrastructure powers critical financial operations across banking technology solutions, merchant acquisition and payment processing, global commerce platforms, billing and payment systems, and point-of-sale services.

Operating across multiple global markets, the client manages a complex product portfolio requiring frequent updates and releases. Its mission-critical systems demand zero-defect deployment standards while meeting regulatory compliance requirements across numerous jurisdictions.

Strategic Imperatives & Challenges

Resource and Cost Constraints

  • Manual test case authoring consumed excessive man-hours
  • High operational costs associated with traditional documentation-to-test conversion
  • Scarce Subject Matter Expert (SME) time spent on repetitive tasks rather than strategic work

Quality and Consistency Issues

  • Human variability led to inconsistent test case quality across teams
  • Coverage gaps in test scenarios risked defects reaching production
  • Lack of objective metrics to measure test completeness
  • No standardized approach to identify missing test scenarios

Speed-to-Market Challenges

  • Lengthy manual test planning created bottlenecks that delayed product releases
  • Competitive pressure demanding faster feature deployment
  • Testing activities starting too late in the development lifecycle

Looking to automate test case creation and reduce QA effort using GenAI?

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Our Approach

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QualityKiosk Technologies deployed an AI-powered platform that automatically converts product documentation into structured, comprehensive test cases. The solution combines a commercial, cloud-based large language model (LLM) with our domain expertise to deliver high-quality test artifacts with measurable coverage. Advanced Natural Language Processing (NLP) analyzes Business Requirements Documents and User Guides, generating complete test case structures that integrate seamlessly into Agile/DevOps workflows.

Our Implementation Framework:

  • Documentation Ingestions

    Secure parsing of product documentation into AI-ready format

  • Automated Test Case Generation

    The LLM generates complete test case details including Summary, Description, Expected Results, and Test Type

  • Validation & Coverage Analysis

    A proprietary module calculates percentage coverage and identifies gaps

  • SME Review and Enhancement

    SMEs use coverage suggestions to optionally supplement test cases without mandatory manual creation

  • Test Artifact Export

    Final structured test cases exported into their Test Management System

Key differentiators include our proprietary Domain Knowledge Layer that refines LLM output with testing best practices, comprehensive detail generation covering full test case structure, our Validation and Coverage Analysis Module with built-in coverage analysis and percentage metrics, and ‘shift-left‘ testing enablement for earlier test planning in the development lifecycle.

Operational Efficiency

Accelerated Release Cycles
Achieved 40-50% faster releases, with performance varying based on automation scope, framework efficiency, and application complexity
Time Savings
Reduced manual QA operations from 24 hours to 6 hours per cycle, a 75% reduction in testing time
Enhanced Defect Management
Achieved defect leakage below 2% (compared to the baseline of 41%), maintaining 99-100% defect detection efficiency consistently over 4-5 months.
Rapid Issue Detection
Reduced Mean Time to Detect Defects (MTTD) by over 90% for faster resolution and prevention of customer-facing issues
Comprehensive Coverage
Parallel execution expanded testing across multiple OS versions and devices, and lowered compatibility risks

Results Achieved

The implementation delivered measurable improvements across efficiency, quality, and cost metrics:

Effort and Time Optimization

  • Significant reduction in manual test case authoring hours
  • Accelerated test planning cycles enabling earlier defect detection
  • Freed SME capacity for high-value exploratory testing and architecture review

Quality Enhancement

  • Improved test coverage through systematic scenario identification
  • Consistent test case quality eliminating human variability

Cost Impact

  • Reduced operational costs through automation of repetitive tasks
  • Lower defect remediation costs from comprehensive pre-production testing
  • Optimized resource allocation across QA functions

Process Transformation

  • Introduction of objective, data-driven metrics into QA workflows
  • Shift-left testing integration enabling proactive quality management
  • Standardized test case format across teams and projects

Business Impact

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Accelerated time-to-market through faster product releases and shortened QA cycles

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Enhanced competitive positioning via rapid feature deployment 

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Significant reduction in post-production defects improving customer satisfaction and brand reputation 

The shift to data-driven quality management provided operational scalability through a repeatable process that maintains consistent quality across an expanding product portfolio, reduces dependency on individual SME knowledge, and creates a foundation for continuous quality improvement.

Schedule a discussion with our QA experts to discuss how we can accelerate your testing workflow.

BAN-AIDO-2147

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