The rapid expansion of digital banking ecosystems has intensified the demand for real-time fraud detection architectures capable of operating at cloud scale. As financial transactions increasingly traverse mobile platforms, API-driven services, embedded finance channels, and cross-border payment networks, fraud patterns have become more dynamic, decentralized, and behaviorally complex. This shift has exposed the limitations of legacy rule-based systems, which lack the adaptability, latency tolerance, and threat-intelligence integration required to counter emerging risks. To address these challenges, cloud-native risk orchestration layers have emerged as a foundational component of next-generation fraud detection, delivering high-throughput data ingestion, elastic compute, and intelligent decisioning frameworks suited for modern digital banking environments. At a broader level, cloud-native risk orchestration unifies distributed event streams, machine-learning scoring engines, and policy-management modules within a scalable, microservices-based architecture. This enables fraud systems to process high-velocity transactional, behavioral, and device-identity signals with millisecond latency. As the narrative narrows, the paper explores how real-time fraud detection leverages cloud services such as serverless functions, container orchestration, distributed caching, and streaming analytics to enable adaptive detection pipelines. It further examines how federated intelligence, feature stores, and continuous learning loops enhance model accuracy while maintaining compliance with privacy and data-residency requirements. At its core, the proposed framework emphasizes explainability, risk transparency, and operational resilience incorporating alert-triage routing, anomaly-suppression mechanisms, decision traceability, and integration with case-management workflows. By combining cloud-native design principles with advanced fraud analytics, the paper outlines a comprehensive blueprint for financial institutions seeking to modernize their risk-management stack. This unified approach offers a path toward scalable, real-time, and intelligence-driven fraud prevention that adapts to evolving threats while supporting regulatory compliance and customer trust.