IRTED-TL An Inter-Region Tax Evasion Detection Method Based on Transfer Learning

Xulyu Zhu, Zheng Yan, Jianfei Ruan, Qinghua Zheng, Bo Dong

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

2 Citations (Scopus)
173 Downloads (Pure)

Abstract

Tax evasion detection plays a crucial role in addressing tax revenue loss. Many efforts have been made to develop tax evasion detection models by leveraging machine learning techniques, but they have not constructed a uniform model for different geographical regions because an ample supply of training examples is a fundamental prerequisite for an effective detection model. When sufficient tax data are not readily available, the development of a representative detection model is more difficult due to unequal feature distributions in different regions. Existing methods face a challenge in explaining and tracing derived results. To overcome these challenges, we propose an Inter-Region Tax Evasion Detection method based on Transfer Learning (IRTED-TL), which is optimized to simultaneously augment training data and induce interpretability into the detection model. We exploit evasion-related knowledge in one region and leverage transfer learning techniques to reinforce the tax evasion detection tasks of other regions in which training examples are lacking. We provide a unified framework that takes advantage of auxiliary data using a transfer learning mechanism and builds an interpretable classifier for inter-region tax evasion detection. Experimental tests based on real-world tax data demonstrate that the IRTED-TL can detect tax evaders with higher accuracy and better interpretability than existing methods.

Original languageEnglish
Title of host publicationProceedings - 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018
PublisherIEEE
Pages1224-1235
Number of pages12
ISBN (Electronic)978-1-5386-4388-4
DOIs
Publication statusPublished - 5 Sep 2018
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Trust, Security and Privacy in Computing and Communications / IEEE International Conference on Big Data Science and Engineering - New York, United States
Duration: 1 Aug 20183 Aug 2018

Publication series

NameIEEE International Conference on Trust, Security and Privacy in Computing and Communications
ISSN (Electronic)2324-9013

Conference

ConferenceIEEE International Conference on Trust, Security and Privacy in Computing and Communications / IEEE International Conference on Big Data Science and Engineering
Abbreviated titleTrustcom/BigDataSE
CountryUnited States
CityNew York
Period01/08/201803/08/2018

Keywords

  • inter-region detection
  • interpretability
  • tax evasion
  • transfer learning

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  • Projects

    Digitalizing Trust for Securing Pervasive Social Networking

    Yan, Z.

    01/09/201731/08/2022

    Project: Academy of Finland: Other research funding

    Cite this

    Zhu, X., Yan, Z., Ruan, J., Zheng, Q., & Dong, B. (2018). IRTED-TL An Inter-Region Tax Evasion Detection Method Based on Transfer Learning. In Proceedings - 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018 (pp. 1224-1235). [8456038] (IEEE International Conference on Trust, Security and Privacy in Computing and Communications). IEEE. https://doi.org/10.1109/TrustCom/BigDataSE.2018.00169