Using data analytics to catch GST evadersDate: 14 January 2020 Tags: IT, Mobile & Computers
The Department of Revenue has identified as many as 931 cases of fraudulent GST (Goods and Services Tax) refund claims through data analytics.
Identified taxpayers, who had purchased goods from tax-evading, non-filers, would face verification and scrutiny as necessary.
It was also through data analytics that recently, GST formations had identified a few exporters with ‘star’ status who were fraudulently availing IGST refund and were untraceable at their registered addresses.
The GST data analytics wing had been able to identify all such cases involving fake invoicing and fraudulent tax credits, which have been encashed through the facility of IGST refunds.
Data analysis is a process of inspecting, cleansing, transforming and modeling data with the goal of discovering useful information, informing conclusion and supporting decision-making. Data integration is a precursor to data analysis,[according to whom?] and data analysis is closely linked[how?] to data visualization and data dissemination.
Several cities all over the world have employed predictive analysis in predicting areas that would likely witness a surge in crime with the use of geographical data and historical data.
Using thye past travel records to predict and analyse the most suitable and safe path for movement of people in a large area.
Fraud and Risk detection
banks learning to divide and conquer data from their customers’ profiles, recent expenditure and other significant information that were made available to them
This made it easy for them to analyze and infer if there was any probability of customers defaulting.
These days, analytical software is used for detecting the various forms of fraudulent claims. Risky claims are detected by red flag indicators which can be examined. It is very essential to bring such claims to the attention of administrators, due to the manner at which automation is improving claims processing efficiency.
Machine and instrument data use has risen drastically so as to optimize and track treatment, patient flow as well as the use of equipment in hospitals.
Data analytics applications would target where taxpayers’ money would have a major impact on and the kind of work that would be adequate for it.