Detecting Tax Evaders Using TrustRank and Spectral Clustering

Mehta, Priya and Mathews, Jithin and Bisht, Dikshant and Suryamukhi, K. and Kumar, Sandeep and Babu, Ch Sobhan (2020) Detecting Tax Evaders Using TrustRank and Spectral Clustering. Lecture Notes in Business Information Processing, 389. pp. 169-183. ISSN 1865-1348

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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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IITH Creators:

IITH Creators	ORCiD
Mehta, Priya	UNSPECIFIED
Methews, Jithin	UNSPECIFIED
Bisht, D.	UNSPECIFIED
Suryamukhi, K.	UNSPECIFIED

Item Type:

Article

Uncontrolled Keywords:

Business interactions; Correlation parameters; Economic growths; Iron and steel; Kernel Density Estimation; Spectral clustering; Tax evasions; Tax returns

Subjects:

Computer science

Divisions:

Department of Computer Science & Engineering

Depositing User:

. LibTrainee 2021

Date Deposited:

17 Jul 2021 09:17

Last Modified:

17 Jul 2021 09:17

URI:

http://raiithold.iith.ac.in/id/eprint/8398