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Detecting Tax Evaders Using TrustRank and Spectral Clustering

Conference paper
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Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 389)

Abstract

Indirect taxation is a significant source of livelihood for any nation. Tax evasion inhibits the economic growth of a nation. It creates a substantial loss of much needed public revenue. We design a method to single out taxpayers who evade indirect tax by dodging their tax returns. Towards this, we derive six correlation parameters (features), three ratio parameters from tax return statements submitted by taxpayers, and another parameter based on the business interactions among taxpayers using the TrustRank algorithm. Then we perform spectral clustering on taxpayers using these ten parameters (features). We identify taxpayers located at the boundary of each cluster by using kernel density estimation, which are further investigated to single out tax evaders. We applied our method on the iron and steel taxpayer’s data set provided by the Commercial Taxes Department, Government of Telangana, India.

Keywords

Cluster analysis TrustRank algorithm Spectral clustering Social network analysis Tax evasion Goods and Services Tax 

Notes

Acknowledgment

We express our sincere gratitude to the Telangana state Government, India, for sharing the commercial tax data set, which is used in this work. This work has been supported by Visvesvaraya Ph.D. Scheme for Electronics and IT, Media Lab Asia, grant number EE/2015-16/023/MLB/MZAK/0176.

References

  1. 1.
    Yuan, S., Wu, X., Li, J., Lu, A.: Spectrum-based deep neural networks for fraud detection. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (CIKM 2017), pp. 2419–2422. ACM, New York (2017).  https://doi.org/10.1145/3132847.3133139
  2. 2.
    Godbole Committee: Report on Economic Reforms of Jammu and Kashmir. Ministry of Finance, Government of Jammu and Kashmir (1998)Google Scholar
  3. 3.
    Bianchi, P.A., et al.: Professional Networks and Client Tax Avoidance: Evidence from the Italian Statutory Audit Regime, SSRN (2016). https://ssrn.com/abstract=2601570
  4. 4.
    Assylbekov, Z., Melnykov, I., Bekishev, R., Baltabayeva, A., Bissengaliyeva, D., Mamlin, E.: Detecting value-added tax evasion by business entities of Kazakhstan. In: Czarnowski, I., Caballero, A.M., Howlett, R.J., Jain, L.C. (eds.) Intelligent Decision Technologies 2016. SIST, vol. 56, pp. 37–49. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-39630-9_4CrossRefGoogle Scholar
  5. 5.
    Mathews, J., Mehta, P., Kuchibhotla, S., Bisht, D., Chintapalli, S.B., Visweswara Rao, S.V.K.: Regression analysis towards estimating tax evasion in goods and services tax. In: 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI), pp. 758–761, Santiago (2018)Google Scholar
  6. 6.
    Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. 41, 3, Article 15, 58 (2009).  https://doi.org/10.1145/1541880.1541882
  7. 7.
    Sahin, Y., Duman, E.: Detecting credit card fraud by ANN and logistic regression. In: 2011 International Symposium on Innovations in Intelligent Systems and Applications. IEEE, June 2011. ISBN 978-1-61284-919-5Google Scholar
  8. 8.
    Gyöngyi, Z., Garcia-Molina, H., Pedersen, J.: Combating web spam with trustrank. In: Nascimento, M.A., Özsu, M.T., Kossmann, D., Miller, R.J., Blakeley, J.A., Schiefer, K.B., (eds.) Proceedings of the Thirtieth International Conference on Very Large Data Bases, VLDB Endowment (VLDB 2004), vol. 30, pp. 576–587 (2004)Google Scholar
  9. 9.
    Wang, J., Zhou, S., Guan, J.: Detecting potential collusive cliques in futures markets based on trading behaviors from real data. Neurocomputing 92, 44–53 (2012)CrossRefGoogle Scholar
  10. 10.
    Issa, H., Vasarhelyi, M.A.: Application of anomaly detection techniques to identify fraudulent refunds (2011).  https://doi.org/10.2139/ssrn.1910468
  11. 11.
    de Roux, D., Perez, B., Moreno, A., del Pilar Villamil, M., Figueroa, C.: Tax fraud detection for under-reporting declarations using an unsupervised machine learning approach. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2018), pp. 215–222. ACM, New York (2018)Google Scholar
  12. 12.
    González, P.C., Velásquez, J.C.: Characterization and detection of taxpayers with false invoices using data mining techniques. Expert Syst. Appl. 40(5), 1427–1436 (2013)CrossRefGoogle Scholar
  13. 13.
    Golub, G., Pereyra, V.: Separable nonlinear least squares: the variable projection method and its applications. Inverse Problems (IOP) 19(2), R1–R26 (2003)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Rad, M.S., Shahbahrami, A.: High performance implementation of tax fraud detection algorithm. In: Signal Processing and Intelligent Systems Conference (SPIS), pp. 6–9, Tehran (2015)Google Scholar
  15. 15.
    Ng, A.Y., Jordan, M.I., Weiss, Y.: On spectral clustering: analysis and an algorithm. In: Advances in Neural Information Processing Systems, pp. 849–856 (2002)Google Scholar
  16. 16.
    Tran, L.T.: The \(L_1\) convergence of kernel density estimates under dependence. Can. J. Stat./La Revue Canadienne de Statistique 17, 197–208 (1989). http://www.jstor.org/stable/3314848
  17. 17.
    Rousseeuw, P.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20(1), 53–65 (1987).  https://doi.org/10.1016/0377-0427(87)90125-7CrossRefzbMATHGoogle Scholar
  18. 18.
    Ketchen, D.J., Shook, C.L.: The application of cluster analysis in strategic management research: an analysis and critique. Strateg. Manag. J. 17(6), 441–458 (1996)CrossRefGoogle Scholar
  19. 19.
    Luxburg, U.: A tutorial on spectral clustering. Stat. Comput. 17(4), 395–416 (2007)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Dani, S.: A research paper on an impact of goods and services tax on indian economy. Bus. Econ. J. 7(4), 1–2 (2016)MathSciNetGoogle Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Indian Institute of Technology HyderabadSangareddyIndia
  2. 2.Plianto TechnologiesSangareddyIndia

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