Predictive Validation of Banking APIs and Transaction Workflows Using Machine Learning-Based Defect Detection Model

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 de