3 ways AI can take on money launderers more effectively


It is estimated that around 2 to 5 percent of global GDP, or $800 billion to $2 trillion a year fall victim to money laundering. In the UK, money laundering is out of control with an estimated £100 billion flowing through the country each year. Yet recent reports show there has not been a single prosecution for money laundering since new regulations were introduced in 2017. 

1. Reduce false positives

Legacy systems have been great at providing compliance officers with mountains of paperwork. It is their job to sift through all the suspicious transactions coughed up by computers. ‍The problem is that most of the transactions are perfectly innocent but every single red flag, and there can be hundreds of thousands produced in a matter of hours, must be assessed individually by a human. While this is not an impossible task, it is extremely time consuming. Worse still, a real threat could pass through the net due to the sheer workload that people have to deal with on a daily basis. ‍

Fortunately, the vast majority of red flags produced by legacy systems should never have been raised in the first place, they are often merely a product of a system that is falsely matching data to incorrect criteria. ‍With today’s technology, we now know that there are far more efficient ways to detect suspicious behaviours. Modern technology such as AI is vastly more sophisticated and, if employed correctly, can reduce volumes of false positives considerably.

‍If modern technology can drastically reduce the volumes of false positives, and pop up more accurate threat identification, not only will a larger proportion of real alerts be reported, but it also won’t take as long to review them.

2. Put compliance in the driving seat

With AI becoming more and more advanced, everyone and their dog has claimed to implement it within their own business in some way. It has become a fashionable sort of thing. This type of ‍AI isn’t so much a set of rules or the ability to create rules. It goes much further than that. AI-based platforms can sift through far more data and adapt without the need for further human instruction, which only slows things down.

Methods of laundering money change constantly. Launderers stay ahead of the authorities in every way. They are extremely cunning in their methods, tweaking their ways to stay just one step ahead of the authorities. Compliance officers are often in the dark about exactly what it is they are looking for, and with the vast and increasing numbers of transactions passing through bank accounts on a daily basis, they are sitting on a ticking time bomb in the race to catch one of these suspected fraudsters.

However, with AI, searching for the ‘unknown unknowns’ becomes ever more achievable. Machine learning algorithms can change the way compliance officers find patterns of suspicious activity. AI programmes are so advanced, they can achieve all this in an instant, testing and adapting thousands of times quicker than any human ever could. ‍Ultimately this puts the compliance officer in the driving seat with a chance to pre-empt a money launderer’s next move. It’s a game changer!

3. Improve Intelligence

Apart from false positives and/or false negatives, there are many other challenges faced by those on the hunt for money laundering activities. ‍What happens after that is also somewhat worrying. Intelligence gathered from accurate analysis of transaction monitoring is right at the front-end in the fight against financial crime. ‍However, law enforcement agencies are showered with more reports of suspicious activity (SARs), many of these low quality reports, than they could ever deal with.

The 2018 National Crime Agency report on SARs shows that, between April 2017 and March 2018, the NCA received 463,938 SARs alone. ‍The explosion in SARs could be driven by a desire by those who must comply with AML regulations to show they are trying to detect wrongdoing. Add to this the fact that ineffective transaction monitoring systems tend to generate too many alerts (as we’ve already described), and the picture of over-reporting becomes clearer as multitudes of (false) alerts are investigated and consequently reported.

‍Although SARs are key to helping law enforcement fight financial crime, over-reporting hampers efforts all around. The current defensive reporting style is a significant burden on workloads and budgets for organisations and law enforcement alike. ‍AI will allow companies to strip back the redundant SARs with confidence, allowing analysts to direct their attention to the transactions more worthy of investigation.
‍It follows that fewer alerts will allow time for more thorough investigation. Consequently, more accurate intelligence will be passed on which will put law enforcement agencies in a stronger position to take on the battle against financial crime.

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