Financial Fraud: Identifying Corporate Tax Report Fraud Under the Xgboost Algorithm - Li, Xianjuan (EAI Endorsed Transactions on Scalable Information Systems, 2023)
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.
Keywords: financial fraud; corporate tax; falsification identification; XGBoost algorithm