Research on Corporate Financial Anomaly Detection and Early Warning System Based on SVM ‐ LightBGM

As enterprise operations become more complex and financial data volumes grow rapidly, traditional methods for detecting financial anomalies are increasingly inadequate for management needs. This study proposes a new model for financial anomaly detection and early warning. The model is based on a hybrid algorithm that combines LightGBM and Support Vector Machine (SVM). It uses a layered structure. The first layer employs SVM to classify financial data and generate probability distributions of abnormal behavior. The second layer applies LightGBM to analyze these results along with key financial features. This enhances the model's efficiency and accuracy in identifying anomalies. The model is tested using a public financial dataset, which includes enterprise financial statements from the past 5 years. Evaluation metrics include accuracy, recall, F1 score, and AUC. Results show that the LightGBM‐SVM model performs significantly better than traditional and single algorithms. Specifically, the model achieves an accuracy of 93.2%, a recall of 95.4%, and an AUC exceeding 98.2%. The model's parameters are optimized to handle large‐scale data efficiently. It also demonstrates high sensitivity and strong generalization in detecting anomalies. Based on these findings, the study designs a complete financial anomaly detection and early warning system. The system includes modules for data preprocessing, feature extraction, model training and detection, and real‐time alerts. It can clean and standardize historical financial data, extract relevant features, and generate real‐time warnings based on the model's output. This provides an intelligent and efficient solution for enterprise financial management. The system proves highly applicable in real‐world scenarios. It improves the accuracy of anomaly detection and significantly lowers the cost of manual reviews. Future research may further improve the model's computational efficiency and explore its use in other domains.