Home Accounting and Auditing How to catch big-time tax cheats with graphs

How to catch big-time tax cheats with graphs

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It’s common knowledge that big-time corporate tax cheats are able to hide revenue and profits by routing funds through shell and front companies. Forensic investigators can usually penetrate just two or three layers into the pyramid schemes constructed by the cheats. In this article from Accounting Today, Gaurav Deshpande explains how graphs are being used to crack the matrix.

Conditions worldwide are ripe for tax evasion. Modern technology has facilitated easy movement of money across international borders, driving tremendous velocity and growth for the global economy.

Unfortunately, it also allows tax evaders to set up 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 Cayman Islands, Panama, and the Bahamas — further complicate the issue, limiting the amount of information shared by these governments with the U.S., 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 — organized online crime rings — 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.

The result is a complicated hub of connections, with multiple layers of relationships hidden within data.

Traditional fraud investigation solutions built on the relational databases struggle to go beyond two or three levels of data, 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, but struggle as the layers of the tax evasion trail expands to four or more levels.

The criminals set up new corporations or subsidiaries of an existing corporation and use it to launder the money to and from the tax havens before shutting 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

Native parallel graph databases are built for digging as much as ten or more levels deep into the money trail, and 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 asOpenCorporates — 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, native parallel graphs (such as this) are capable of analyzing the money flow through 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 in the native parallel graphs.

As criminals deploy complex strategies and modern technology for tax evasion, this technology can be used effectively by the IRS and other agencies all over the world to catch the crooks.