This study focuses on the problems of poor real-time and limited accuracy of traditional data processing methods in the context of enterprise financial risk management, and builds a set of real-time enterprise financial big data processing and risk management system based on a distributed computing framework. The system adopts Spark as the core distributed computing architecture, and achieves a peak processing capacity of up to 47,500 items/second and an average processing delay of only 267 milliseconds in a 12-node configuration, which greatly improves the efficiency of data processing. The real-time risk management module of the system shows significant advantages in risk identification, prediction, control and feedback, with the risk identification rate reaching 91.2%, the warning accuracy rate reaching 87.4%, and the length of warning advance extending to 18.6 days. By building a multi-level and multi-dimensional risk assessment modeling system and integrating static financial analysis and dynamic transaction monitoring, the system achieves the function of dynamic detection of risk status and accurate early warning. At the level of data security and privacy protection, the system utilizes data watermarking technology in distributed environment, computing task security sandbox, and privacy computing framework based on homomorphic encryption to ensure data security and privacy.
