Abstract
Tax evasion is committed in a number of ways, in which, some are easily identifiable while few others are very difficult to detect. This article deals with a sophisticated technique used by tax-evaders known as circular trading. Dealers who commit this fraud often collude together and make bogus companies using fraudulent identities with the motivation to show heavy sales transaction among them. This huge quantity of data from transactions helps the dealers to hide their actual tax manipulation. Here, we devise clustering techniques that detects and groups together the dealers who are highly susceptible in performing circular trading. We represented the entire sales database for these dealers using weighted directed graphs. The clustering algorithm is run on the commercial tax dataset provided by the state government of Telangana, India, which helped in identifying potential circular trading activities.
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Dani, S.: A research paper on an Impact of Goods and Service Tax (GST) on Indian Economy. Bus. Econ. J. 7, 264 (2016)
Godbole Committee: Report on Economic Reforms of Jammu and Kashmir. Ministry of Finance, Government of Jammu and Kashmir (1998)
Schenk, A., Oldman, O.: Value Added Tax: A Comparative Approach, 1st edn. Cambridge Tax Law Series (2007)
Palshikar, G.K., Apte, M.M.: Collusion set detection using graph clustering. Data Min. Knowl. Discov. 16(2), 135–164 (2008)
Franke, M., Hoser, B., Schröder, J.: On the analysis of irregular stock market trading behavior. In: Data Analysis, Machine Learning and Applications, pp. 355–362 (2007)
Golmohammadi, K., Zaiane, O.R., Daz, D.: Detecting stock market manipulation using supervised learning algorithms. In: Data Science and Advanced Analytics, pp. 435–441. IEEE (2014)
Wang, J., Zhou, S., Guan, J.: Detecting potential collusive cliques in futures markets based on trading behaviors from real data. J. Neurocomput. 92, 44–53 (2012)
Vicente, E., Mateos, A., Jimnez-Martn, A.: Detecting stock market manipulation using supervised learning algorithms. In: Modeling Decisions for Artificial Intelligence, pp. 205–216. Springer (2016)
Assylbekov, Z., et al.: Detecting value-added tax evasion by Business Entities of Kazakhstan. In: Intelligent Decision Technologies, pp. 37–49. Springer (2016)
Hsu, K.W., Pathak, N., et al.: 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 (2014)
Mehta, P., Mathews, J., et al.: A graph theoretical approach for identifying fraudulent transactions in circular trading. In: The Sixth International Conference on Data Analytics, Barcelona, Spain (2017)
Edmonds, J., Karp, R.M.: Theoretical improvements in algorithmic efficiency for network flow problems. J. ACM Assoc. Comput. Mach. 19(2), 248–264 (1972)
Acknowledgment
We are very grateful to the Telangana state government, India, for sharing the commercial tax dataset, which is used in the proposed work. This work has been supported by Visvesvaraya PhD Scheme for Electronics and IT, Media Lab Asia, grant number EE/2015-16/023/MLB/MZAK/0176.
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Mathews, J., Mehta, P., Sobhan Babu, C., Kasi Visweswara Rao, S.V. (2019). Clustering Collusive Dealers in Commercial Taxation System. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2018. Advances in Intelligent Systems and Computing, vol 869. Springer, Cham. https://doi.org/10.1007/978-3-030-01057-7_54
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DOI: https://doi.org/10.1007/978-3-030-01057-7_54
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