15 Apr The $458 Billion Per Year Tax Evasion Problem – Can Graph Database and Analytics Help?
As the deadline to file the taxes, April 15th looms large in the United States, I have been thinking about paying our fair share of taxes and what we can do to find the individuals and corporations who employ all sorts of tricks to get out of paying their fair share. Tax evasion is a growing issue, with the annual revenue loss for the United States government estimated at over US $458 Billion annually.
Side effects of a well connected global economy
The modern technology has facilitated easy movement of money across the international borders driving tremendous velocity and growth for the global economy. Unfortunately, it also allows the tax evaders to set up the shell corporations with just a few clicks on the internet and an encrypted phone call to the criminals who make these corporations look like the legitimate entities. The local laws in tax havens such as the British Virgin Islands further complicate the issue, limiting the amount of information shared by these governments with US, EU, and other tax authorities.
Setting up the shell corporations to evade taxes with Crime-as-a-service
Shell corporations are entities set up with the express purpose of hiding the income and avoiding the taxes for that income. Crime-as-a-service has become a reality, with the sophisticated fraudsters incorporating new companies with fake or paid directors hiding the actual beneficiary, the tax evader. They route the money through an intricate trail of the accounts for these shell corporations and passing the proceeds or income back via an equally complex path to avoid the detection. Traditional fraud investigation solutions built on the relational databases struggle to go beyond two or three levels, as every level requires computationally expensive and time-consuming database joins. The first and second generation graph databases are great at finding the money trail up to three levels, however, struggle as the layers of the tax evasion trail expands to four or more levels.
The explosion of shell corporations – “the fireflies of tax evasion”
The criminals set up new corporations or subsidiaries of an existing corporation, use it to launder the money to and from the tax havens and then shut down these subsidiaries, making it very difficult to find or track the money movement through these “fireflies of tax evasion”. This requires the tax fraud detection solution to understand the structure of corporate entities with three or more layers, identify changes to the structure over time (“temporal analysis”) and flag suspicious patterns where specific subsidiaries were used for a short period of time for routing money and were shut down after that period.
Finding the tax evaders with the native parallel graph database and analytics
The native parallel graph databases are built for digging as much as 10 or more levels deep into the money trail, identifying the shell corporations that have similar or identical addresses, contact numbers, share one or more directors and have been created or administered from the same set of IP addresses. They are also adept at the temporal analysis of complex corporate hierarchies, identifying subsidiaries that are used for a very short period of time for passing funds back and forth to a related set of accounts who all seem to transact only with each other. Native parallel graphs are also capable of incorporating data from multiple internal and external sources such as OpenCorporates, the world’s largest open database of corporate information. This is useful in finding and connecting common directors among companies from multiple sources as well as common or similar addresses, phone numbers, and other contact information. Lastly, the native parallel graphs are capable of analyzing the money flow through the accounts with as many as 10 or more hops, understand the loopbacks through an equally complex path and identify suspicious patterns that seem to indicate tax evasion. This is powered by the massively parallel processing (MPP) in the native parallel graphs.
As the criminals deploy complex strategies and modern technology for tax evasion, the native parallel graphs can be used by the US Internal Revenue Service (IRS) and other agencies all over the world to find those activities. Learn more about this and other capabilities of the native parallel graphs in the native parallel graphs eBook.
*This blog was originally posted on Accounting Today*