Detecting value-added tax evasion by business entities of Kazakhstan

Zhenisbek Assylbekov, Igor Melnykov, Rustam Bekishev, Assel Baltabayeva, Dariya Bissengaliyeva, Eldar Mamlin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Scopus citations

Abstract

This paper presents a statistics-based method for detecting value-added tax evasion by Kazakhstani legal entities. Starting from features selection we perform an initial exploratory data analysis using Kohonen self-organizing maps; this allows us to make basic assumptions on the nature of tax compliant companies. Then we select a statistical model and propose an algorithm to estimate its parameters in unsupervisedmanner. Statistical approach appears to benefit the task of detecting tax evasion: our model outperforms the scoring model used by the State Revenue Committee of the Republic of Kazakhstan demonstrating significantly closer association between scores and audit results.

Original languageEnglish (US)
Title of host publicationIntelligent Decision Technologies 2016 - Proceedings of the 8th KES International Conference on Intelligent Decision Technologies, KES-IDT 2016
EditorsLakhmi C. Jain, Robert J. Howlett, Ireneusz Czarnowski, Alfonso Mateos Caballero, Lakhmi C. Jain, Lakhmi C. Jain
PublisherSpringer Science and Business Media Deutschland GmbH
Pages37-49
Number of pages13
ISBN (Print)9783319396293
DOIs
StatePublished - 2016
Externally publishedYes
Event8th KES International Conference on Intelligent Decision Technologies, KES-IDT 2016 - Puerto de la Cruz, Tenerife, Spain
Duration: Jun 15 2016Jun 17 2016

Publication series

NameSmart Innovation, Systems and Technologies
Volume56
ISSN (Print)2190-3018
ISSN (Electronic)2190-3026

Conference

Conference8th KES International Conference on Intelligent Decision Technologies, KES-IDT 2016
CountrySpain
CityPuerto de la Cruz, Tenerife
Period6/15/166/17/16

Keywords

  • Anomaly detection
  • Cluster analysis
  • Self-organizing maps
  • Tax evasion detection

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