Governments worldwide are under mounting pressure to tighten their tax gaps, defined as the difference between what the government expects to collect in taxes versus what is actually collected. Closer to home, in a 2011 Economic Transformation Program (ETP) update by Minister in the PM’s Department and CEO of PEMANDU, Dato’ Sri Idris Jala, Malaysia’s tax gap stands at 20%.
Based on the country’s 2014 direct tax collection of RM133.7 billion, a 20% tax gap leaves an estimated (and whopping) RM33 billion worth of taxes uncollected and unaccounted for.
In contrast, many developed nations have tax gaps hovering between 10% – 15%.
Fraud is a major component of a nation’s tax gap, and it comes in many flavors. People and companies evade taxes by underreporting their income. Moreover, they tend to underpay or avoid filing their taxes altogether. The hidden economy – such as money laundering, prostitution, arms and drug trafficking – is also another key reason for a country’s tax gap.
In the past, efforts to widen the field audit and investigation coverage to narrow the tax gap were met with limited success due to manpower constraints and difficulties in monitoring and identifying these fraudulent individuals and organizations.
This is about to change. Governments are increasingly turning to sophisticated techniques like big data analytics to identify tax fraud and increase its tax revenue.
Numerous advanced data analytics tax collection business cases have been developed in recent years. These include initiatives such as propensity to pay, tax collection risk scoring, anti-money laundering, and GST fraud identification.
The Tax Big Data Analytics Maturity Model (Figure 1) shows that high-impact tax business cases that will bring in serious returns for the government – like GST fraud identification, anti-money laundering and real-time fraud detection – will require the deployment of sophisticated analytics that goes beyond traditional reporting, statistical and predictive models. These high-value projects will require the implementation of advanced data analytics such as text mining, path analysis, connections, affinity and visualization.
Figure 1: Tax Big Data Analytics Maturity Model (Image Credit: Teradata)
Let’s take GST scams as an example. The government will be able to use big data analytics to clamp down on companies that charge GST but do not pay the output tax. They will also be able to use advanced analytics techniques to detect GST carousel fraud (Figure 2).
Traditionally, it is impossible for tax fraud analysts to scrutinize millions of transaction data points. The execution of the carousel fraud, for instance, usually takes place in just a few weeks, and by the time it is detected by tax agents, the fraudsters would have gone missing with stolen tax money.
With big data analytics, millions of transactions can be automatically processed. A graph data model can be built to represent GST fraud. Powerful visualization tools can be used to present the chains of suspicious transactions. Fraud analysts can drill down to see companies and individuals connected to these transactions. These suspicious cases can then be flagged for audit. Anti-fraud and investigation teams will be able to act faster to avoid the crime from being committed.
Yep, the taxman is comin’ after you with big data.
Figure 2: An Example of a GST Carousel Fraud (Image Credit: Inland Revenue Authority of Singapore)