Research on the Model of Preventing Corporate Financial Fraud under the Combination of Deep Learning and SHAP

Abstract: Preventing financial fraud in listed companies is conducive to improving the healthy development of China’s accounting industry and the securities market, is conducive to promoting the improvement of the internal control system of China’s enterprises, and is conducive to promoting stability. Based on the combination of SHAP (Shapley Additive explanation), a prediction and identification model should be built to determine the possibility of financial fraud and the risk of fraud for the company. The research model has effectively improved the identification accuracy of financial fraud in listed companies, and the research model has effectively dealt with the gray sample problem that is common in the forecasting model through the LOF algorithm and the IF algorithm. When conducting comparative experiments on the models, the overall accuracy rate of the research model is over 85%, the recall rate is 78.5%, the precision rate is 42%, the AUC reaches 0.896, the discrimination degree KS reaches 0.652, and the model stability PSI is 0.088, compared with traditional financial fraud Forecasting models FS model and CS model has a higher predictive effect. In the empirical analysis, selecting a company’s fraud cases in 2020 can effectively analyze the characteristic contribution in the fraud process and the focus on fraud risks. The established model can effectively monitor the company’s finance and prevent fraud.

Yanzhao Wang. “Research on the Model of Preventing Corporate Financial Fraud under the Combination of Deep Learning and SHAP”. International Journal of Advanced Computer Science and Applications (IJACSA) 14.3 (2023). http://dx.doi.org/10.14569/IJACSA.2023.0140390

@article{Wang2023,
title = {Research on the Model of Preventing Corporate Financial Fraud under the Combination of Deep Learning and SHAP},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140390},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140390},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {3},
author = {Yanzhao Wang}
}