The complexity and uncertainty of the business operating environment are increasingly prominent in the current era of big data. The traditional financial risk management paradigm, which relies on lagging financial data, is increasingly unable to meet real-world demands. Therefore, this study aims to construct a more accurate and dynamic financial risk early warning model by deeply mining multi-source heterogeneous data from both internal and external sources of the enterprise and applying big data analysis techniques. This requires exploring the application advantages of big data technology in risk identification, measurement, and monitoring. Based on this, an integrated warning indicator system will be designed by combining core financial indicators with unstructured data, such as online public opinion and industry chain information. By utilizing machine learning algorithms, a warning model will be constructed and optimized to deeply learn the patterns of risk evolution, enabling real-time detection and proactive warnings of potential financial risks. Finally, a systematic response strategy will be proposed based on the warning signals of the model, covering preemptive measures, in-process control, and post-event management, to enhance the risk management capabilities of enterprises and provide guidance that holds both theoretical value and practical significance for achieving stable operations.