Most discussions surrounding the usage of big data focus on the benefits it would add to creating more targeted ads, or the ability to improve sales tactics. However, one growing area where big data has proven its efficiency is in crime fighting.
Back in 2013, Business Insider ran an article on how law enforcement was using big data to prevent crime. Weve also learned the U.S. Federal Government is using data from Twitter to crack down on hate speech and financial institutions are using data analytics as a means of detecting fraud. The human mind can only handle so much, but with improvements in computer technology, law officials now have the ability to crack down on crime much more efficiently than in the past.
Take money laundering. In recent years it has grown to be a massive problem, mainly because of the adaptive nature of criminals. In the past, criminals relied on banks and the financial system to smuggle their funds. However, increased governmental oversight and international anti-money laundering efforts have caused these criminals to switch gears and turn to global trade in order to sneak their money across borders.
Trade-Based Money Laundering: The Weakest Link
According to a study referenced by PwC in an article by CIO, around 80 percent of illicit funds from developing countries are done through trade-based commerce, which The Economist dubbed the weakest link in the fight against dirty money.
Trade-Based Money Laundering (TBML) is defined as using commerce to get money across borders. It can be as simple as drug dealers using their profits to buy and import clothing from another country, then liquidating it in order to get it into the banking system. The reason TBML has become so popular is because its still very paper-based, lacking the structure and technology of modern business operations.
In order to remain undetected, criminals will combine and rotate between different tactics. Some of the more common TBML strategies include:
Over-invoicing – Here, a criminal would invoice trade goods at prices that exceed their market value
Multiple invoicing – This process involves a launderer issuing multiple invoices for the same transaction.
Over- and under-shipment – This tactic simply means over or understating the number of goods shipped with regards to payment sent or received.
False description of trade goods – This technique involves misrepresenting the type of good, or its quality, like replacing expensive clothing with cheap knockoffs, but still charging a premium price.
Big Data to the Rescue
So the question inevitably follows, what can big data do? How can analytics peel back the layers of a $18 billion industry and find these illegal transactions? Some might suggest a better solution would be to impose global regulations on international trade, much like what happened with banks, to combat money laundering. However, this process would also hinder those conducting legitimate business transactions. Thats why data analytics are perfect. They can analyze trade without hindering its flow. There are a number of data techniques that could reveal anomalies and suspicious activity, based on models and other inputs by officials. Some of these techniques include:
Web analytics and web-crawling – This means searching the web to review shipment details and comparing them with other documentation to see if they measure up properly.
Unit-price analysis – Using this method, officials could combat over or under invoicing. The algorithm would compare documents to publicly available data and detect if prices are too high or too low.
Unit-weight analysis – Similar to the process for unit-price analysis, this technique is designed to detect over or under shipment. Algorithms would compare information to public data to see if weights match recorded information and if the quantity of goods matched payments.
One of the greatest benefits of recent developments in big data technology is the ability to process data in real-time. Instead of pouring over mounds of documents and cross-checking information, a program can do it all much faster, leading to quicker detections rates and placing a chokehold on criminal activity.
However, as mentioned at the beginning, there are many practical uses for big data analytics. Whatever industry your organization falls within, theres definitely a benefit to using available information. The key is to invest in tools accessible for users with various skill sets and experience levels. Researching the best platforms, and comparing things like Amazon Elastic MapReduces performance with other services, will ensure you find the best program to improve your chances of success.
