Microservices-Based Enterprise Financial Risk Early Warning System
Zixuan Yu · Advances in transdisciplinary engineering · 2025
With the expansion of financial market scale and the complexity of risk patterns, the traditional risk assessment system is difficult to meet the current demand in terms of data processing and scalability. This study builds a distributed system with elastic scalability based on Spring Cloud microservice framework and combined with Docker containerized deployment. By introducing TiDB distributed database and Kafka message queue, reliable storage and real-time analysis of financial data are realized. The system uses the improved Z-Score model and LSTM deep learning network for risk assessment, and the risk warning accuracy rate reaches 92.3%. In the performance test, the system shows excellent concurrent processing capability and stability, with the number of concurrent users on a single node reaching 5,000, service availability reaching 99.99%, and the message processing rate reaching 15,000 messages per second, which provides financial institutions with an efficient and reliable risk assessment solution.