THE REVENUE COMMISSIONERS have said that the hi-tech methods employed to track tax dodging should not be confused with simply creeping on Facebook.
The Finance Minister yesterday revealed that tax evasion was being tracked through “social network analysis”. However, the Revenue Commissioners say that that goes beyond looking to see if you have a picture of a shiny new car on your Facebook.
A spokesperson told TheJournal.ie that the office was moving with the times to catch those who avoided tax.
“In an increasingly digitalised economy, Revenue uses all the tools available to identify and target non-compliance risks. Using advanced IT system we assess risks, whether at a transactional, individual taxpayer or business sector level, and from that we determine the type of compliance intervention that best addresses the risk”.
The spokesperson added that there are four key approaches.
Social Network Analysis:
“A recent addition to Revenue’s risk evaluation approach is Social Network Analysis. This approach should not be confused Twitter or Facebook!
This technology uses our own data and other data available to Revenue to identify links and relationships between individuals and businesses.
In essence SNA allows complex networks of individuals and businesses to be easily visualised and understood.”
Risk, Evaluation, Analysis and Profiling system (REAP):
“REAP cross-checks and interrogates all the various data sources we have available on individual taxpayers and businesses, and risk ranks the taxpayer population, automatically highlighting the particular issues that need to be examined in the context of Revenue’s tax compliance activities.”
Real-Time Risk Analytics
“Real-time risk analytics ensure that transactions are risk assessed in real time i.e. as the transactions are processed.
A combination of predictive models and specific business rules are applied to transactions and behaviours, to identify compliance risks such as fraudulent refund or repayment claims.
“Revenue is also developing and maintaining other predictive models using advanced data mining techniques. These models are used to identify risky cases by analysing historical risk indicators and then predicting the future behaviour of cases. The models are used for case selection and decision support.”