AI-Enhanced API Reliability Testing for Digital Banking: Improving Accuracy, Resilience, and Integrity in Financial Transaction Processing
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V6I2P116Keywords:
Application Programming Interfaces (APIs), Reliability Testing, Digital Banking, Machine Learning, Convolutional Neural Networks, Recurrent Neural Networks, Ensemble Techniques, Fault Prediction, Agile Lifecycle GovernanceAbstract
The rapid proliferation of digital banking ecosystems has elevated application programming interfaces (APIs) to the status of critical infrastructure. Ensuring the reliability, accuracy, and resilience of these APIs is paramount for maintaining the integrity of financial transaction processing. Traditional deterministic testing paradigms are increasingly inadequate for navigating the complex, asynchronous, and high-velocity nature of modern microservices architectures. This paper presents a comprehensive, AI-enhanced framework for API reliability testing that leverages a converged architecture of Machine Learning (ML) algorithms, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and advanced ensemble techniques. By predicting software faults before deployment and dynamically modeling service dependencies using graph theory, the proposed framework shifts the paradigm from reactive defect discovery to predictive quality assurance. Empirical evaluation on simulated high-frequency banking telemetry demonstrates that the integration of boosting and voting methods significantly enhances fault prediction accuracy, achieving an F1-score of 0.94. Furthermore, the incorporation of decision intelligence methodologies ensures agile software lifecycle governance and optimized automation economics. Ultimately, this research provides a robust, scalable, and highly accurate methodology for securing digital banking interfaces against catastrophic failure and cybersecurity threats, ensuring uninterrupted financial integrity.
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