Circular Trading: Detection Algorithm Based on Graph Theory

Abstract

“Circular Trading” has emerged as a new form of tax evasion in Goods and Service Tax regime. The tax payers are removing goods without paying taxes and then mask these illegal transactions with circular trading with related firms. This results in inadmissible input tax credit to unsuspecting B2B buyer, tax fraud with government and fudged data submitted to financial institutions for loan. This mal-practice is causing tax loss to the government, creating audit problems to the Chartered Accountants and generating huge amount of black money and parallel economy.

This is a new and model research with the purpose to develop a computer algorithm to detect such mal-practice. In order to do so, the paper goes to create a mathematical model using ‘graph theory’ and computer algorithm to detect such transactions. The full algorithm to detect the transactions involved in circular trading is part of this paper. By using this algorithm, the various concerned parties like Government, Chartered Accountant and B2B buyers can detect the genuineness of transaction and easily separate circular trading.

The study is based on live case study and subsequent expert analysis of the situation. Then data were masked over actual data to hide the identity of firms involved and resulted algorithm was tested on these data. The algorithm is limited to small set of data, but it can be easily expended to very large set of data and employed in practical world.

The computer algorithm suggested has been tested using actual programming in C# and SQL Server. The algorithms to detect the circular trading in system has been defined using directed graph.