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sis 23(4): e10

Research Article

Financial Fraud: Identifying Corporate Tax Report Fraud Under the Xgboost Algorithm

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  • @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
Xianjuan Li1,*
  • 1: Hunan City University
*Contact email: juanwei3312@yeah.net

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.

Keywords
financial fraud, corporate tax, falsification identification, XGBoost algorithm
Received
2023-02-14
Accepted
2023-04-18
Published
2023-05-05
Publisher
EAI
http://dx.doi.org/10.4108/eetsis.v10i3.3033

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.

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