Preventing Tax Evasion of the Commercial Tax Departments through Statistical Models

Abstract

Billions of dollars across the Tax administrations around the world are lost every year due to noncompliance, evasions, frauds or non-collection including both direct and indirect taxes. With access to vast quantities of data from a range of sources (e.g. financial institutions, utilities, bank transactions, social media data, etc.) both in terms of structured as well as unstructured (text, video, pdfs, etc.), tax authorities can increasingly use business rules, quantitative statistical models and advance analytics techniques to conduct audits and uncover trends and discrepancies, using new techniques such as rulebased monitoring, predictive modelling and outlier detection. This paper will showcase how the tax authorities should be moving into the predictive mode rather than post audit reactive mode for zeroing on the probable risky dealers in the indirect tax domain for highest impact of revenue recovery and collection through a statistical, scientific and information driven model for decision making.