Tax Underreporting Detection Using an Unsupervised Learning Approach

Vitali Herrera-Semenets, Lázaro Bustio-Martínez, Jorge Ángel González-Ordiano, Jan van den Berg

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Abstract

Governmental adminstrative domains can potentially benefit from a wide variety of currently available big data analysis methods. The tax administration is such an area that requires massive data processing to identify hidden patterns and trends of possible tax evasion. The use of supervised methods can be effective in these cases, but the lack of available labeled data limits their practical application in real-world scenarios. An alternative is the use of unsupervised methods, which have potential benefits in certain cases. In this sense, unsupervised methods are considered to be feasible as a decision support tool in tax evasion risk management systems. This paper proposes an unsupervised approach to identify signs of tax evasion by detecting, possible, tax underreporting. The proposed strategy is evaluated on a data set associated with individual income tax statistics of the United States. The results achieved are considered to be useful in decision-making and preventive actions on cases reported as suspicious.
Original languageEnglish
Title of host publicationAdvances in Soft Computing - 23rd Mexican International Conference on Artificial Intelligence, MICAI 2024, Proceedings
Subtitle of host publication23rd Mexican International Conference on Artificial Intelligence, MICAI 2024, Tonantzintla, Mexico, October 21–25, 2024, Proceedings, Part II
EditorsLourdes Martínez-Villaseñor, Gilberto Ochoa-Ruiz
Place of PublicationCham
PublisherSpringer
Pages16-28
Number of pages13
ISBN (Electronic)978-3-031-75543-9
ISBN (Print)978-3-031-75542-2
DOIs
Publication statusPublished - 2024
Event23rd Mexican International Conference on Artificial Intelligence - Tonantzintla, Mexico
Duration: 21 Oct 202425 Oct 2024
Conference number: 23rd

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume15247 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference23rd Mexican International Conference on Artificial Intelligence
Country/TerritoryMexico
CityTonantzintla
Period21/10/2425/10/24

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • clustering
  • tax evasion
  • tax underreporting
  • unsupervised classification

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