(untitled) (International Journal of Engineering & Extended Technologies Research (IJEETR), 2026)
The rapid adoption of cloud-native technologies in financial systems has transformed how institutions manage, analyze, and secure data. However, this shift introduces new risks related to fraud, cyber threats, and operational inefficiencies. Leveraging machine learning (ML) for fraud detection provides an intelligent, adaptive approach capable of identifying anomalous transactions in real-time, mitigating financial losses, and enhancing trust. Simultaneously, API-driven decision frameworks enable seamless integration, orchestrating data flow between cloud-native components while facilitating automated, data-driven decision-making processes. This paper explores the convergence of secure cloud-native financial architectures with advanced ML fraud detection models and intelligent APIs, highlighting architectural best practices, risk mitigation strategies, and performance optimization techniques. We discuss the methodologies employed in developing predictive models, the role of continuous monitoring, and the implementation of resilient API frameworks that support real-time decision-making. By analyzing current literature and case studies, this research presents a holistic framework for financial organizations to achieve secure, scalable, and intelligent operational capabilities. The proposed approach demonstrates significant potential to improve fraud detection accuracy, system reliability, and operational efficiency in modern cloud-native financial ecosystems.