A Scalable AI-Driven Quality Engineering Architecture for End-To-End Validation of Core Banking, API, and UAT Ecosystems
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V7I6P113Keywords:
Quality Engineering, Core Banking Systems, User Acceptance Testing (Uat), Api Ecosystems, Machine Learning, Decision Intelligence, Defect Prediction, Agile Software Lifecycle, Systems EngineeringAbstract
The modernization of financial institutions has catalyzed the transition from monolithic legacy systems to highly distributed, API-centric core banking architectures. While this evolution enables unprecedented scalability and integration, it introduces profound complexities in Quality Engineering (QE). Traditional validation methodologies, particularly within User Acceptance Testing (UAT) and API integration phases, are inherently unscalable, labor-intensive, and prone to missing complex, systemic defects. This paper proposes a highly scalable, AI-driven Quality Engineering architecture designed for the end-to-end validation of core banking systems. By converging Machine Learning (ML) defect prediction models, dynamic service dependency graphs, and automated decision intelligence, the proposed framework drastically optimizes the UAT ecosystem. Empirical evaluations utilizing simulated high-frequency banking telemetry demonstrate that the integration of advanced ensemble techniques (Random Forest and Gradient Boosting) paired with graph-based test case prioritization reduces UAT cycle times by 68% while achieving a defect detection F1-score of 0.93. The architecture seamlessly integrates predictive quality assurance into the agile software lifecycle, ensuring that core banking upgrades, API deployments, and complex UAT scenarios are executed with maximum integrity, minimal automation economics overhead, and fortified cybersecurity resilience. Ultimately, this research provides a comprehensive blueprint for financial institutions seeking to achieve autonomous, predictive, and infinitely scalable quality engineering.
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