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Detecting Value-Added Tax Evasion by Business Entities of Kazakhstan

  • Zhenisbek AssylbekovEmail author
  • Igor Melnykov
  • Rustam Bekishev
  • Assel Baltabayeva
  • Dariya Bissengaliyeva
  • Eldar Mamlin
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 56)

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 unsupervised manner. 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.

Keywords

Self-organizing maps Cluster analysis Anomaly detection Tax evasion detection 

Notes

Acknowledgments

We would like to thank Inês Russinho Mouga for the thorough review of [9].

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Zhenisbek Assylbekov
    • 1
    Email author
  • Igor Melnykov
    • 1
  • Rustam Bekishev
    • 1
  • Assel Baltabayeva
    • 2
  • Dariya Bissengaliyeva
    • 2
  • Eldar Mamlin
    • 2
  1. 1.School of Science and TechnologyNazarbayev UniversityAstanaKazakhstan
  2. 2.State Revenue Committee, Ministry of Finance of KazakhstanAstanaKazakhstan

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