Abstract
INTRODUCTION: With the development of economy, the phenomenon of financial fraud has become more and more frequent. OBJECTIVES: This paper aims to study the identification of corporate tax report falsification. METHODS: Firstly, financial fraud was briefly introduced; then, samples were selected from CSMAR database, 18 indicators related to fraud were selected from corporate tax reports, and 13 indicators were retained after information screening; finally, the XGBoost algorithm was used to recognize tax report falsification. RESULTS: The XGBoost algorithm had the highest accuracy rate (94.55%) when identifying corporate tax statement falsification, and the accuracy of the other algorithms such as the Logistic regressive algorithm were below 90%; the F1 value of the XGBoost algorithm was also high, reaching 90.1%; it also had the shortest running time (55 s). CONCLUSION: The results prove the reliability of the XGBoost algorithm in the identification of corporate tax report falsification. It can be applied in practice.
Publisher
European Alliance for Innovation n.o.
Subject
Information Systems and Management,Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Software
Reference34 articles.
1. Wang D, Lin J, Cui P, Jia Q, Wang Z, Fang Y, Yu Q, Zhou J, Yang S, Qi Y. A Semi-Supervised Graph Attentive Network for Financial Fraud Detection. 2019 IEEE International Conference on Data Mining (ICDM); 2019. p. 598-607.
2. Cheng C H, Kao Y F, Lin H P. A financial statement fraud model based on synthesized attribute selection and a dataset with missing values and imbalanced classes. Appl. Soft Comput., 2021; 108(3):1-19.
3. Heneke E, Valentine R, Jourdan Z. Predictive Factors in Financial Fraud and Malfeasance from 1950-2018. J. Bus. Econ. Perspect., 2021; 48(1):1-21.
4. Voznyak H V. Financial Fraud in the Budget Sphere: Economic Essence and Varieties. Bus. Inform, 2020; 4(507):334-339.
5. Wu H, Chang Y, Li J, Zhu X. Financial fraud risk analysis based on audit information knowledge graph. Proc. Comput. Sci., 2022; 199:780-787.