Predictive Modeling for Identifying Return Defaulters in Goods and Services Tax

Mehta, Priya and Mathews, Jithin and K, Suryamukhi and K, Sandeep Kumar and Ch, Sobhan Babu (2018) Predictive Modeling for Identifying Return Defaulters in Goods and Services Tax. In: 5th IEEE International Conference on Data Science and Advanced Analytics, DSAA, 1-4 October 2018, Turin, Italy.

Full text not available from this repository. (Request a copy)


Tax evasion is an illegal practice where a person or a business entity intentionally avoids paying his/her true tax liability. Any business entity is required by the law to file their tax return statements following a periodical schedule. Avoiding to file the tax return statement is one among the most rudimentary forms of tax evasion. The dealers committing tax evasion in such a way are called return defaulters. In this paper, we construct a logistic regression model that predicts with high accuracy whether a business entity is a potential return defaulter for the upcoming tax-filing period. For the same, we analyzed the effect of the amount of sales/purchases transactions among the business entities (dealers) and the mean absolute deviation (MAD) value of the first digit Benford's law on sales transactions by a business entity. We developed this model for the commercial taxes department, government of Telangana, India.

[error in script]
IITH Creators:
IITH CreatorsORCiD
Item Type: Conference or Workshop Item (Paper)
Subjects: Computer science
Divisions: Department of Computer Science & Engineering
Depositing User: Team Library
Date Deposited: 29 Mar 2019 06:25
Last Modified: 29 Mar 2019 06:25
Publisher URL:
Related URLs:

Actions (login required)

View Item View Item
Statistics for RAIITH ePrint 4924 Statistics for this ePrint Item