학술논문

Identifying Malicious Dealers in Goods and Services Tax
Document Type
Conference
Source
2019 IEEE 4th International Conference on Big Data Analytics (ICBDA) Big Data Analytics (ICBDA), 2019 IEEE 4th International Conference on. :312-316 Mar, 2019
Subject
Computing and Processing
Finance
Anomaly detection
Feature extraction
Government
Clustering algorithms
Computer science
goods and services tax
value added tax
fraud detection
malicious dealers
clustering
Language
Abstract
Tax evasion is pervasive in most of the nations. It impedes the economic progress of the nation. In this paper, we devise a technique that detects groups of people who commit evasion in the goods and services tax by manipulating their sales and (or) purchases with the motivation to reduce their tax liability. In particular, we perform clustering analysis on certain sensitive parameters pertaining to each of the dealers. Then by analyzing the number of dealers across the different clusters, we identified the suspicious set of dealers who are further investigated by looking at other sensitive parameters. This work is designed and implemented for the Government of Telangana, India.