Regression Analysis towards Estimating Tax Evasion in Goods and Services Tax

Tax evasion is as old as tax itself. In this paper, we devise a technique to predict the amount of tax-revenue lost by the state due to unscrupulous actions from a particular set of suspicious dealers. For the same, we build a regression model using the tax-return information of genuine business dealers and predict the amount of tax evaded by suspicious business dealers. Dealers are classified as genuine or suspicious by applying Benford's analysis on the different group of dealers formed after running k-medoids clustering algorithm over a set of dealers. In addition to getting an estimate on the loss of tax-revenue, results obtained from this work aid the tax enforcement officers on taking precautionary measures against tax evasion. The dataset used in the work is provided by the commercial tax department of Telangana state, India.