Against the backdrop of the rapid development of big data technology, enterprise internal control and risk management face new challenges. The traditional static internal control mechanism focusing on process control can no longer cope with the complex situation of multi-source data and changing risks. This paper, from the perspective of big data drive, deeply analyzes the limitations of current enterprise internal control in terms of data island status, risk identification lag and rigid response mechanism, and advocates the construction of an enterprise-level internal control risk governance model that integrates graph neural network (GNN), multi-source data fusion processing and real-time monitoring technology. The model design includes business semantic extraction, risk scoring mechanism, and dynamic early warning response architecture. In the technical implementation link, the streaming computing platform, rule engine and RPA process control means are introduced to achieve a closed-loop internal control management mechanism of "pre-warning, in-process governance, and post-audit". The study further introduces the data governance mechanism and risk culture construction measures in the strategic dimension, so as to ensure the long-term development of the system and the fit of the organization. The results of this study have key theoretical and practical significance in enhancing the digital governance level of enterprises, improving compliance capabilities and improving risk control efficiency.

Hongxi Wu. Research on the Construction of Enterprise Internal Control Risk Management Model and Real-Time Monitoring Technology Driven by Big Data. Socio-Economic Statistics Research (2025), Vol. 6, Issue 1: 13-22. https://doi.org/10.38007/SESR.2025.060102.

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