Architectural Overview of Cloud-Native Automated Compliance Reporting System for Distributed Trading Platforms

Abstract

This article presents a comprehensive architectural framework for implementing automated compliance reporting systems within distributed trading platforms, addressing the complex regulatory requirements imposed by MiFID II, Dodd-Frank, and Basel III. The article explores critical architectural foundations, including immutable logging mechanisms, distributed tracing patterns, secure data lake designs, and data lineage tracking that form the backbone of modern compliance automation. Through detailed analysis of cloud-native workflow orchestration using Apache Flink for real-time stream processing and Apache Airflow for batch reporting, the article demonstrates how financial institutions can achieve regulatory adherence while maintaining system performance and scalability. The article explores operational resilience strategies encompassing reconciliation frameworks, failover mechanisms, trade modification tracking, and comprehensive testing approaches that ensure data integrity across distributed environments. Implementation strategies are outlined through phased deployment methodologies, performance benchmarking considerations, and emerging technology adoption, including artificial intelligence and blockchain integration. The findings provide practical guidance for financial institutions seeking to transform their compliance operations from reactive manual processes to proactive automated systems that balance regulatory requirements with operational efficiency.