Cloud-Native Event-Driven Orchestration Architecture for Real-Time Fraud and Sanctions Screening

Abstract

The rapid digital transformation of financial services has significantly increased transaction volumes and complexity, exposing financial institutions to growing risks such as fraud, money laundering, identity theft, terrorist financing, and sanctions violations. Traditional centralized fraud detection systems are unable to meet the real-time scalability, latency, and compliance requirements of modern cloud-based financial platforms. This paper proposes a Cloud-Native Event-Driven Orchestration Architecture that integrates event streaming, microservices, AI-driven fraud detection, sanctions screening, risk scoring, and automated decision-making into a unified, scalable framework. The architecture continuously processes transaction events, enriches contextual information, performs real-time fraud and sanctions analysis, and routes transactions for approval, rejection, or manual review. It also incorporates monitoring, observability, fault tolerance, and dynamic resource management to ensure high availability. Mathematical models for fraud risk, sanctions similarity, and orchestration efficiency support quantitative evaluation using metrics such as detection accuracy, latency, throughput, scalability, compliance, and reliability. The proposed framework delivers faster processing, improved fraud detection, stronger regulatory compliance, and greater operational resilience for next-generation financial systems.