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

• Published in: International journal of grid and high performance computing Vol. 17; no. 1; pp. 1 - 22
• Main Authors: Yi, Zhishuai, Liu, Piao
• Format: Journal Article
• Language: English
• Published: Hershey IGI Global 25.09.2025
• Subjects: Accuracy; Anomalies; Big Data; Colleges & universities; Data analysis; Early warning systems; Real time
• ISSN: 1938-0259; 1938-0267; 1938-0267
• DOI: 10.4018/IJGHPC.388950

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.