DESIGNING A HIGH-THROUGHPUT MICROSERVICES PLATFORM FOR FINANCIAL RISK DATA ENRICHMENT: A CASE STUDY

Abstract

Financial institutions increasingly rely on rapidly processing vast, heterogeneous data streams for effective risk management and regulatory compliance. However, integrating and enriching this data in near real-time presents significant architectural challenges, particularly at scale. This paper details the design, implementation, and impact of a high-availability, microservices-based platform developed to automate data processing for a central Eastern European bank. The primary objective was to create a modular, scalable, and fault-tolerant system capable of managing massive data volumes while ensuring data integrity and low-latency delivery of insights. A case study methodology was employed to document the system's architecture. The platform utilizes an event-driven, asynchronous model built with Java and the Spring Boot framework. Core components include decoupled microservices for data ingestion, normalization, enrichment, and delivery, orchestrated via RabbitMQ message queues. A novel dispatcher service was implemented to manage entity-level concurrency control, preventing race conditions during parallel data processing. The system's performance and health are monitored through an integrated observability stack comprising Prometheus, Grafana, Loki, and Zipkin. The platform successfully processes over 160 million data units daily from over 20 disparate sources. It reduced data processing latency from several days to under 15 minutes and consistently meets a critical service-level agreement (SLA) of delivering search query results in less than 10 seconds. Automation eliminated manual data handling, reduced data quality error rates by approximately 87%, and significantly enhanced the bank’s ability to detect adverse financial events in near real-time. This case study validates the efficacy of a microservices architecture combined with explicit concurrency control for building high-throughput FinTech data platforms. The presented design is a practical blueprint for engineering resilient, scalable, and observable systems to address complex data integration challenges in the financial sector and other dataintensive industries.