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.
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References
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
Godbole Committee: Report on Economic Reforms of Jammu and Kashmir. Ministry of Finance, Government of Jammu and Kashmir (1998)
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
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_4
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)
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
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 978-1-61284-919-5
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)
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)
Issa, H., Vasarhelyi, M.A.: Application of anomaly detection techniques to identify fraudulent refunds (2011). https://doi.org/10.2139/ssrn.1910468
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)
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)
Golub, G., Pereyra, V.: Separable nonlinear least squares: the variable projection method and its applications. Inverse Problems (IOP) 19(2), R1–R26 (2003)
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)
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)
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
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)90125-7
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)
Luxburg, U.: A tutorial on spectral clustering. Stat. Comput. 17(4), 395–416 (2007)
Dani, S.: A research paper on an impact of goods and services tax on indian economy. Bus. Econ. J. 7(4), 1–2 (2016)
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.
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Mehta, P., Mathews, J., Bisht, D., Suryamukhi, K., Kumar, S., Babu, C.S. (2020). Detecting Tax Evaders Using TrustRank and Spectral Clustering. In: Abramowicz, W., Klein, G. (eds) Business Information Systems. BIS 2020. Lecture Notes in Business Information Processing, vol 389. Springer, Cham. https://doi.org/10.1007/978-3-030-53337-3_13
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