Financial Risk Early Warning System for Colleges and Universities Based on Big Data Analysis

At present, financial risk early warning systems in colleges and universities lack the ability to process real-time data flow, making it difficult to capture short-term risk fluctuations in a timely manner and limiting their accuracy in short-term forecasting. This study builds a real-time data pipeline based on Apache Kafka and Spark Streaming. Short-term financial index prediction and risk classification are realized by combining a bidirectional long short-term memory network with the XGBoost model. In addition, anomaly detection and dynamic threshold adaptive adjustment are carried out through isolated forests to improve the real-time performance and prediction accuracy of the system. Experiments show that the highest rate of misjudgment is about 2.5% under the robustness test, the cross-school accuracy of migration is over 80%, consistency with auditor hits is over 78.5%, and the average detection rate in real-time stream detection is 83.3%. The results of this study verify the efficiency and adaptability of the system.