  Circular trading is a form of tax evasion in Goods and Services Tax where a
group of fraudulent taxpayers (traders) aims to mask illegal transactions by
superimposing several fictitious transactions (where no value is added to the
goods or service) among themselves in a short period. Due to the vast database
of taxpayers, it is infeasible for authorities to manually identify groups of
circular traders and the illegitimate transactions they are involved in. This
work uses big data analytics and graph representation learning techniques to
propose a framework to identify communities of circular traders and isolate the
illegitimate transactions in the respective communities. Our approach is tested
on real-life data provided by the Department of Commercial Taxes, Government of
Telangana, India, where we uncovered several communities of circular traders.
