WOA-XGBoost-based financial statement fraud detection

Xinfeng Dou; Rong Liu; Shengpeng Yin

doi:10.1117/12.3032976

5 July 2024 WOA-XGBoost-based financial statement fraud detection

Xinfeng Dou, Rong Liu, Shengpeng Yin

Proceedings Volume 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024); 131846L (2024) https://doi.org/10.1117/12.3032976
Event: 3rd International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 2024, Kuala Lumpur, Malaysia

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xinfeng Dou, Rong Liu, and Shengpeng Yin "WOA-XGBoost-based financial statement fraud detection", Proc. SPIE 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 131846L (5 July 2024); https://doi.org/10.1117/12.3032976

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