Year: 2018
Type: dissertation
Abstract: Tax evasion is an illegal practice where a person or a business entity intentionally avoids paying \nhis/her true tax liability. Any business entity is required by the law to file their tax return statements \nfollowing a periodical schedule. Avoiding to file the tax return statement is one among the most \nrudimentary forms of tax evasion. The dealers committing tax evasion in such a way are called return \ndefaulters. We constructed a logistic regression model that predicts with high accuracy whether a \nbusiness entity is a potential return defaulter for the upcoming tax-filing period. For the same, \nwe analyzed the effect of the amount of sales/purchases transactions among the business entities \n(dealers) and the mean absolute deviation (MAD) value of the �rst digit Benford's analysis on sales \ntransactions by a business entity. We developed and deployed this model for the commercial taxes \ndepartment, government of Telangana, India. Another technique, which is a much more sophisticated \none, used for tax evasion, is known as Circular trading. Circular trading is a fraudulent trading \nscheme used by notorious tax evaders with the motivation to trick the tax enforcement authorities \nfrom identifying their suspicious transactions. Dealers make use of this technique to collude with each \nother and hence do heavy illegitimate trade among themselves to hide suspicious sales transactions. \nWe developed an algorithm to detect the group of colluding dealers who do heavy illegitimate trading \namong themselves. For the same, we formulated the problem as finding clusters in a weighted directed \ngraph. Novelty of our approach is that we used Benford's analysis to define weights and defined \na measure similar to F1 score to find similarity between two clusters. The proposed algorithm is \nrun on the commercial tax data set, and the results obtained contains a group of several colluding \ndealers.
Authors: Sandeep Kumar K, Sobhan Babu
Language: English
Subfield: Economics and Econometrics
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W2844549954