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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Joint Order of the Minister of Finance of the Republic of Kazakhstan dated September 16, 2011, # 468, and acting Minister of Economic Development and Trade of the Republic of Kazakhstan dated September 16, 2011, # 302, “On approval of the risk assessment criteria in the field of private enterprise on the execution of tax laws, and other laws of the Republic of Kazakhstan, control over the execution of which is entrusted to the tax authorities” (has been canceled on December 25, 2015).
- 2.
The RoK tax forms can be found at http://kgd.gov.kz/en/section/formy-nalogovoy-otchetnosti.
- 3.
Correlations are negative since the new model assigns lower scores to the companies which are more likely to evade VAT.
References
Anderberg, M.R.: Cluster Analysis for Applications. Monographs and Textbooks on Probability and Mathematical Statistics (1973)
Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. (CSUR) 41(3), 15 (2009)
Dolnicar, S.: The use of neural networks in marketing: market segmentation with self organising feature maps. Proc WSOM 97, 4–6 (1997)
González, P.C., Velásquez, J.D.: Characterization and detection of taxpayers with false invoices using data mining techniques. Expert Syst. Appl. 40(5), 1427–1436 (2013)
Gupta, M., Nagadevara, V.: Audit selection strategy for improving tax compliance: application of data mining techniques. In: Foundations of Risk-Based Audits. Proceedings of the eleventh International Conference on e-Governance, Hyderabad, India, December, pp. 28–30 (2007)
Hsu, K.W., Pathak, N., Srivastava, J., Tschida, G., Bjorklund, E.: Data mining based tax audit selection: a case study of a pilot project at the minnesota department of revenue. In: Real World Data Mining Applications, pp. 221–245. Springer (2015)
Iivarinen, J., Kohonen, T., Kangas, J., Kaski, S.: Visualizing the clusters on the self-organizing map (1994)
Kohonen, T.: The self-organizing map. Neurocomputing 21(1), 1–6 (1998)
Lückeheide, S., Velásquez, J.D., Cerda, L.: Segmentación de los contribuyentes que declaran iva aplicando herramientas de clustering. Revista de Ingeniería de Sistemas 21, 87–110 (2007)
Markey, M.K., Lo, J.Y., Tourassi, G.D., Floyd, C.E.: Self-organizing map for cluster analysis of a breast cancer database. Artif. Intell. Med. 27(2), 113–127 (2003)
Melnykov, V., Chen, W.C., Maitra, R.: MixSim: an R package for simulating data to study performance of clustering algorithms. J. Stat. Softw. 51, 1–25 (2012)
Pampalk, E., Rauber, A., Merkl, D.: Using Smoothed Data Histograms for Cluster Visualization in Self-organizing Maps. Springer (2002)
Squire, D.M., et al.: Visualization of Cluster Changes by Comparing Self-organizing Maps. Springer (2005)
Vesanto, J., Alhoniemi, E.: Clustering of the self-organizing map. IEEE Trans. Neural Netw. 11(3), 586–600 (2000)
Viveros, M.S., Nearhos, J.P., Rothman, M.J.: Applying data mining techniques to a health insurance information system. VLDB 286–294 (1996)
Wehrens, R., Buydens, L.M., et al.: Self-and super-organizing maps in R: the kohonen package. J. Stat. Softw. 21(5), 1–19 (2007)
Williams, G.J., Christen, P., et al.: Exploratory multilevel hot spot analysis: Australian taxation office case study. In: Proceedings of the Sixth Australasian conference on Data mining and analytics, vol. 70, pp. 77–84. Australian Computer Society, Inc. (2007)
Acknowledgments
We would like to thank Inês Russinho Mouga for the thorough review of [9].
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Assylbekov, Z., Melnykov, I., Bekishev, R., Baltabayeva, A., Bissengaliyeva, D., Mamlin, E. (2016). Detecting Value-Added Tax Evasion by Business Entities of Kazakhstan. In: Czarnowski, I., Caballero, A., Howlett, R., Jain, L. (eds) Intelligent Decision Technologies 2016. IDT 2016. Smart Innovation, Systems and Technologies, vol 56. Springer, Cham. https://doi.org/10.1007/978-3-319-39630-9_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-39630-9_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-39629-3
Online ISBN: 978-3-319-39630-9
eBook Packages: EngineeringEngineering (R0)