Research Article
Financial Fraud: Identifying Corporate Tax Report Fraud Under the Xgboost Algorithm
@ARTICLE{10.4108/eetsis.v10i3.3033, author={Xianjuan Li}, title={Financial Fraud: Identifying Corporate Tax Report Fraud Under the Xgboost Algorithm}, journal={EAI Endorsed Transactions on Scalable Information Systems}, volume={10}, number={4}, publisher={EAI}, journal_a={SIS}, year={2023}, month={5}, keywords={financial fraud, corporate tax, falsification identification, XGBoost algorithm}, doi={10.4108/eetsis.v10i3.3033} }- Xianjuan Li
Year: 2023
Financial Fraud: Identifying Corporate Tax Report Fraud Under the Xgboost Algorithm
SIS
EAI
DOI: 10.4108/eetsis.v10i3.3033
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.
Copyright © 2023 Xianjuan Li, licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
