The concept of “money laundering” came to life sometime in the 1930s, by means of organized crime and mafia groups who used it to cover illegal businesses. It wasn’t until the 1960s when the term became a more “household” name adopted by the law.
More importantly, tracking money laundering has always proved challenging, especially in the decades when there were no computers or digital technology. Now that data analytics has emerged as a recognizable industry, it’s now much easier to track criminals involved in fraudulent schemes.
But, how effective can data analytics be in the fight against money laundering? In short, the answer is “very effective as long as it’s used in its proper place”.
Recognizing the Different Types Of Money Laundering
To understand how data analytics can fight money laundering, it’s important to understand how criminals launder money to begin with. There are several techniques that launderers rely on, but some are more common than others.
Common Money Laundering Techniques
- Structuring (a.k.a smurfing) – A method of placement where money launderers break up cash into smaller deposits to reduce suspicion of money laundering.
- Round-tripping – The act of depositing money in a controlled foreign corporation (CFC) that’s offshore, often in a tax haven, where minimal records are stored. These records are shipped back as a foreign direct investment exempt from taxation. A notable example of this is The Panama Papers scandal.
- Bulk cash smuggling – Bulk cash scams involve the act of physically moving cash to another jurisdiction and depositing it in a financial institution where there is less policing of funds. The typical target is an offshore bank.
- Cash-intensive businesses – Many illegal businesses hide behind a “front”, selling a legal product or service while making money from illicit activities in secret. Typical establishments include tanning salons, car washes, adult bars and casinos.
- Trade-based laundering – With trade-based laundering, criminals will either under- or over- value an invoice to mask the movement of illicitly obtained funds.
Other forms of money laundering include the use of “black salaries” (paying unregistered employees cash), certain forms of high-stake gambling and the depositing of funds in obscure shell companies and trusts.
Where Data Analytics Comes Into the Picture
Generally speaking, data analysis tools can’t directly point out to money laundering schemes on their own. It often takes a concentrated and holistic effort involving other fields such as data forensics or even private investigation to bring a money laundering scheme under the spotlight.
However, a data analytics tool is very practical once there is suspicion of a crime. If evidence starts to emerge that money laundering is taking place, then certain datasets can be analyzed to find patterns inconsistent with the typical figures associated with a business operation or department.
The Panama Papers: Data Analytics Lessons To Learn
Many of you remember the Panama Papers scandal that came under the spotlight in the fall of 2015. It was a leak consisting of 11.5 million documents which contained details of financial and attorney-client information for over 214,488 offshore entities. The documents, which were stored at Panamanian law firm, Mossack Fonseca, date back to the 1970s. They detailed several illegal activities involving money laundering, tax evasion and fraud taking place among shell companies.
But how did data analytics come into play? Although it took an anonymous whistleblower to start the leak, data analytics software helped journalists and investigators to crack more documents for further signs of illegal activities.
For example, one specific program allowed the investigators to convert PDFs and images into ‘readable’ information which made it easy to find specific data within each file. That would include information that couldn’t otherwise be retrieved under normal circumstances.
Also, as the stream of data started to increase, a multi-security database was established so that investigators worldwide could access secret documents. Eventually, graph databases were added to visualize data as a series of nodes and connections rather than in a traditional spreadsheet format. This made it easier for journalists and investigators to see patterns among data, allowing them to uncover more illicit activities carried out by the shell companies.
Again, the data analytics software used in this case wasn’t used to discover the Panama Papers, but it was instrumental in making sense of the scandal’s extent once it was uncovered. There are some other takeaways, namely features, that reputable data analytics software such as our GLAnalytics solution can bring to the table.
Features Of Strong Data Analytics Software
- Pulls data from several documents and files so you don’t have to go through each one individually
- Allows multiple departments or professionals to review data once access is granted to them
- Visualizes data in a streamlined and meaningful way that makes it easy to recognize patterns
Data Can Dig Up Dirty Money
If you work in a large organization, it’s important to know the abilities and limitations of data analysis tools. They won’t tell you that money laundering is taking place within your organization, but they can help you reveal its trail if such schemes are already on the radar.
For example, with our GLAnalytics solution, you can collect data from various documents and files, which is presented in an easy-to-interpret display. You can also grant various individuals in your organization access to your data to keep everyone in the loop.
With such a powerful and comprehensive tool at your disposal, you can uncover patterns and insights that will give you a much deeper understanding of what’s taking place in your organization.
