Machine Learning Based Risk Detection and Decision Systems for Financial APIs in Cloud Native Enterprise Architectures
DOI:
https://doi.org/10.15662/IJRAI.2025.0804016Keywords:
Financial APIs, Cloud Native Architecture, Machine Learning, Risk Detection, Decision Systems, Anomaly Detection, Fraud Prevention, Predictive AnalyticsAbstract
The rapid growth of cloud-native enterprise architectures has revolutionized financial services by enabling scalable, resilient, and flexible API-driven ecosystems. However, this transformation has also introduced heightened security and operational risks due to the increased exposure of financial APIs to internal and external threats. This research explores the integration of machine learning (ML) techniques for risk detection and automated decision-making in financial APIs deployed within cloud-native environments. We examine various ML algorithms, including supervised, unsupervised, and reinforcement learning, to detect fraudulent transactions, anomalous patterns, and API misuse. The study also addresses the challenges of real-time data processing, model interpretability, and secure deployment in multi-tenant cloud architectures. By leveraging predictive analytics and anomaly detection, the proposed system aims to enhance risk mitigation, reduce operational losses, and enable proactive decision-making for financial institutions. This paper outlines a comprehensive methodology for implementing ML-driven risk detection systems, evaluates their effectiveness using benchmark datasets, and highlights the advantages of integrating such systems into cloud-native infrastructures. The findings demonstrate that ML-based approaches significantly improve the reliability and security of financial APIs, supporting enterprises in achieving both compliance and operational efficiency in increasingly complex digital financial ecosystems
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