Money laundering is big criminal business worldwide. Banks are tasked by the regulators with reducing the volume and value of money laundering over their services, but that’s easier said than done. In response, many are now starting to use artificial intelligence (AI) to tune results, finding small anomalies within a large amount of data. In the fight against money laundering, banks need both scale and granularity.
In most countries, the regulatory requirements make it difficult to track the success of anti-money laundering (AML) projects, however. Banks are tasked with identifying and investigating potentially fraudulent activity, and disclosing it to the authorities as appropriate. However, there are only two countries worldwide where the authorities will come back and tell the bank what happened – whether they were right. That being the case, how can banks push for greater accuracy in their AML projects when they don’t see the results?
Christopher Ghenne is the Global Lead, Banking & Compliance Solutions at SAS.
The regulatory problem
At the same time, banks in Europe have to contend with a lot of EU AML legislation as well as the laws of their own land. Most institutions have armies of lawyers tasked with wading through the regulations to decipher exactly what the requirements are: what the threshold for reporting should be, what sort of activities and clients the bank should pay attention to, and so on. It’s a complex business.
And that regulatory burden doesn’t stay still. Each country translates EU directives into legislation at parliament level, and then monitoring organisations like the FCA, auditors and law enforcement all put their spin on the legislation.
In the face of so many requirements, banks need to be able to react quickly to useful data if they’re to avoid being fined. It’s not just preventative measures that cause headaches, however. Financial institutions also need to consider the ever-changing mesh of sanctions in place against countries like Russia and Iran, which dictate where money can – or should – be moved at any given time. Many organisations currently struggle to deal with that level of granularity.
The scale of the challenge
It’s estimated that the global money laundering business is worth somewhere in the region of $2000bn, of which only around 0.2% is detected. The majority of banks therefore need advanced analytics software to catch up. The required technology exists, but in many cases it’s not in operation.
It’s not just the scale of the fraudulent activity that’s causing banks a headache, however. There’s also the question of accurate classification of the transactions that do get picked up – false positives often disrupt AML programmes. With advanced analytics and AI in place, banks can tune their algorithms to avoid false positives.
Say, for example, that the average system flags up 100 alerts in a day. In most cases, probably only one or two are actually money laundering cases, as opposed to unusual but legitimate activity. AI can help to make that initial detection more accurate, reducing the number of false positives that come in and giving AML teams more time to deal with genuine alerts.
It’s also essential that banks can demonstrate to regulators why transactions are flagged up in the way they are. How does their segmentation work? Do they use a predictive model, and if so, how do they tune their detection? Institutions have to be able to prove that their decisions are not influenced by unconscious (or conscious) bias. Having a bespoke algorithm in place will give banks the tools they need to clearly lay out why certain actions have been flagged as suspicious.
The AML arms race
We’ve seen, then, that AI holds the keys to a more efficient and transparent AML stance. Despite that, however, money laundering techniques are evolving all the time to get round AML measures. The algorithms aren’t changing fast enough.
Think of AML as being like a game of chess. If you want to win, you have to think several steps in advance. That’s what the money launderers do - as soon as there’s a backdoor or a lag in the process, they take advantage of it, funnelling thousands of dollars through the loophole. When a bank launches a new product, the fraudsters get in before the system has been fully beta tested and patched – by which time they’ve found a new gateway somewhere else.
Finally, banks have to deal with the uncomfortable fact that there are many people and organisations who simply don’t want AML projects to succeed. In all corners of the world, there are huge, underground economic machines that rely on money laundering for their income. These groups are far from powerless in this arms race, and as a result, those who want to reduce money laundering have to move faster and act more intelligently.
AI for effective AML
This is where advanced analytics and AI come in. By providing banks with real-time, in-depth analysis of finance streams, these technologies can provide actionable insights and intelligent anomaly detection at speed.
AI can work at a scale and a pace that adds significantly to AML teams’ ability to keep one step ahead of their adversaries, quickly spotting indicators of fraudulent activity, whether it be an unusually large money transfer to an unfamiliar account or conformation to a known laundering route. AI can also pick up on trends that humans might miss, giving a deeper level of insight than has previously been possible.
Banks must take hold of this new weapon in the fight against money laundering. The rise of online banking has brought with it serious criminal challenges – but now advanced analytics and AI are tipping the scales in favour of the rule of law.
Christopher Ghenne is the Global Lead, Banking & Compliance Solutions at SAS.
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