Predictive Validation of Banking APIs and Transaction Workflows Using Machine Learning-Based Defect Detection Model - Gunda, Sai Kumar (International Journal of Artificial Intelligence, Data Science, and Machine Learning, 2025)
The global transition toward Open Banking and microservices architectures has exponentially increased the reliance on Application Programming Interfaces (APIs) to drive core financial transaction workflows. As financial institutions move away from monolithic core banking systems toward highly distributed ecosystems, the complexity of verifying inter-service communication has surged. Traditional deterministic software testing methodologies—such as static unit testing and manual regression suites—are increasingly insufficient for identifying complex, edge-case defects within these high-velocity, asynchronous banking environments. This paper proposes a novel predictive validation framework leveraging Machine Learning (ML) to proactively detect defects within API codebases and transaction workflows prior to production deployment. By extracting deep code-level metrics, historical commit logs and workflow dependency graphs, the proposed framework employs an optimized ensemble model, specifically combining Random Forest and Gradient Boosting techniques, to predict the statistical probability of runtime failures. Empirical evaluation utilizing simulated, high-frequency banking telemetry demonstrates that the ML-driven approach identifies defect-prone API modules with an F1-score of 0.89, drastically outperforming legacy rule-based QA systems. Initial architectural designs and workflow simulations suggest that shifting from reactive quality assurance to predictive defect modeling significantly reduces post-deployment API downtime, accelerates the agile software lifecycle and preserves the absolute integrity of critical financial workflows. This research formally bridges the gap between software reliability engineering and advanced decision intelligence within the financial sector.