WOA-XGBoost-based financial statement fraud detection
Xinfeng Dou, Rong Liu, Shengpeng Yin · 2024
Financial statement fraud detection is a crucial topic in maintaining financial security. To optimize parameter tuning and improve model performance, we innovatively combined Whale Optimization Algorithm (WOA) with Extreme Gradient Boosting Algorithms (XGBoost) to construct the WOA-XGBoost model. We first construct a XGBoost based classification model based on XGBoost, and adopt the WOA algorithm to adaptively optimize the XGBoost parameters. Compared to manually adjusting the model training parameters, WOA-XGBoost adaptively searches for the optimal parameters. The experimental results presents a noticeable improvement in F1-Score for WOA-XGBoost. Meanwhile, WOA-XGBoost achieved the highest accuracy among the algorithms participating in the comparative experiment, which demonstrating the superior performance of the proposed method.