论文标题
在图表上学习以识别商品和服务税中的循环交易
Representation Learning on Graphs to Identifying Circular Trading in Goods and Services Tax
论文作者
论文摘要
循环交易是商品和服务税逃税的一种形式，其中一组欺诈性纳税人（交易者）旨在通过在短时间内将几项虚拟交易（在商品或服务中添加任何价值）叠加非法交易，以掩盖非法交易。由于纳税人的庞大数据库，当局不可行，可以手动识别循环交易者和他们所涉及的非法交易的群体。这项工作使用大数据分析和图形表示技术来提出一个框架来识别循环交易者社区，并在各种社区中分离非法交易。我们的方法对印度特兰加纳政府商业税部提供的现实生活数据进行了测试，我们在那里发现了几个循环商人社区。
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.
