Predictive Modeling for Identifying Return Defaulters in Goods and Services Tax

Abstract

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.