How Do We Solve Our $1.6 Trillion Laundering Problem?
Can modern technology stem the tide of global money laundering? That's the 1.6 trillion dollar question.
We humans aren’t particularly adept at visualizing large numbers, and anything beyond one hundred is hard to picture in concrete terms. So for a number like one trillion, you can forget about trying to accurately put it into perspective. That’s why when the United Nations Office on Drugs and Crime releases a report saying $1.6 trillion are laundered around the world each year our eyes glaze over and we fail to appreciate just how substantial that amount of money is. For reference, if you were to stack $1 trillion worth of $100 bills on top of each other, it would reach two and a half times higher than the altitude of the International Space Station. It’s a wonder that this unfathomably large amount of money (2.7% of the global GDP, to be exact) is laundered every year with only around .2% of it being seized and frozen. However, when you see the incessant headlines about individuals laundering obscene amounts of money, like this recent DOJ article about a Massachusetts man convicted of laundering $1.4 million, it starts to become clear how much illicit cash is held in the shadows. It is obvious that the financial sector in its current state needs help getting a handle on the epidemic of laundering. Fortunately, we live in an age of rapidly advancing technology like artificial intelligence that we can use to our advantage in the fight against money laundering.
Although scrutinizing millions of dollars’ worth of transactions is a daunting task for any compliance analyst, computers are uniquely suited for such monotonous tasks. AML analysts can be put at ease with the help of an AI enhanced AML infrastructure and their respective financial institution can benefit from a faster and more precise transaction monitoring process. Comarch Anti Money Laundering (CAML), for example, utilizes an analytical AI-based engine to process millions of transactions and scores each one based on the likelihood of it being an incident of money laundering. An analyst would then have the much more practical task of viewing only the transactions that are particularly suspicious. The compliance analysts can be confident that CAML is performing its job effectively by utilizing the administrative module to view statistics and analyze results as well as monitoring the software’s performance.
Software's ability to learn allows it to detect anomalies.
Part of the blame for money laundering getting bad enough to be able to stack the ill-gotten hundreds to a low Earth orbit lies with financial institutions always being a step behind the launderers. It seems every time the financial system cracks down on one method of laundering, another one pops up and is exploited even more than the last. Modern advances in machine learning promise to keep up with the criminals and stomp out new laundering methodologies just as fast as they pop up. CAML utilizes machine learning in order to self-improve and learn from past examples, discovering hidden patterns and understanding relationships and similarities between data that were previously overlooked. CAML’s ability to learn allows it to detect anomalies that it wasn’t previously trained to spot, thereby staying vigilant against laundering.
As money laundering continues to be a scourge on the financial sector, it’s time we start accepting the help of our AI allies who can assist in the overwhelming task of wrestling global money laundering into submission. Right now, the headlines we see are the .2% being caught. Ultimately, the $1.6 trillion question is: Can modern technology stem the tide of global money laundering?
Louis Rossi Director of Business Development at Comarch