Livia Benisty, Banking Circle: AI And The State Of Financial Crime

Livia Benisty, Head of AML at Banking Circle explores…

More than $2 billion in global money laundering fines were issued in 2020. Already five times the amount issued the previous year, the number has continued to rise. Authorities levied almost $1 billion in AML fines in 17 big actions in the first half of 2021 alone.

Increasing fines mean regulators are placing more pressure on financial institutions to improve anti-money laundering (AML) processes, but the numbers are a drop in the ocean when compared to the sheer scale of the problem. With the United Nations estimating that the total amount of money laundered annually to be anywhere between $800 billion – $2 trillion (or 2-5 per cent of global GDP), it’s clear there’s some way to go.

Widening gulf between criminals and large banks

Many of the aforementioned penalties were the result of shortcomings in AML management, monitoring of suspicious activity, and customer due diligence, areas that are now being addressed by the introduction of tighter regulation. The EU, for instance, has announced the introduction of a new AML authority for greater coordination.

Probably the biggest issue, however, is the pace at which technology – and the nature of financial crime – is evolving. If financial institutions don’t keep pace, no matter how seemingly good they get at detecting money laundering, they will always lag behind the criminals. In the constant game of one-upmanship, criminals will always evolve to come up with new tactics and techniques. And, to make the gulf even greater, most of the tools used by the large banks aren’t suitably effective and are typically based on legacy infrastructure – they have their hands tied.

The success of AML can only improve when banks fully embrace digital transformation efforts, such as implementing artificial intelligence (AI).

Focusing on a single element and missing the bigger picture

Traditional rule-based approaches to transaction monitoring are outdated, only capturing one element of a transaction. As a result, they deliver false positive rates of between 97 and 99 per cent. Not only is this hugely inefficient, but it can also be very demotivating for staff as well. After all, how many analysts really want to sift through 99 per cent false positives every day?

Banking Circle’s AI-driven model can consolidate multiple risk factors at once. It assesses multiple dimensions on a transaction, extracting a risk score, and developing an intelligent understanding of what risky behaviour broadly looks like. Indeed, a feedback loop based on advanced analytics means that the more data is collected, the more intelligent the solution becomes.

Rather than using static thresholds, which look for and assess one specific area of risk to determine whether it’s potentially suspicious, it instead looks for a broader array of factors that come together in a payment – something more akin to how money laundering works in the real world. Not only does this reduce false positives, but it also means fewer requests for information on payments – key for an improved customer experience.

In fact, between 2019 and 2021, Banking Circle reduced the amount of false positive rates exponentially. More than 600 accounts were closed or escalated to compliance due to AI-related findings – an increase of 380 per cent. What’s more, as payments rose by 150 percent, the number of alerts generated fell by 30 per cent.

Essentially, by digesting huge amounts of data from multiple sources, the use of AI in AML represents a significantly more efficient and effective mean of detecting suspicious activity. Despite this, though, there is still a lack of understanding about new technologies like AI. Indeed, for many, a fear of the unknown is preventing its wider adoption.

“We’ve always done it this way”

In regulated industries like finance, there can often be a reticence to try anything new. This is based on a belief that regulators want to see tried and tested methods because it’s how it’s always been done. So, investing in new technologies, particularly ones like AI that is seen as an unproven and not widely understood, is considered a risk.

It’s vital, then, to ensure that regulators and financial institutions see the role of AI in AML within the broader framework of digital transformation. For example, in a bid to change attitudes towards AI, it should be made clear that its introduction is not meant to replace humans with machines. Instead, it will empower individuals with tools that will reduce their operational workloads, enhance efficiency, and free up resources to allow focus on other value-adding areas, such as customer relationships.

Attitudes are beginning to change, particularly in the FinTech space, where they’re not burdened with migrating legacy systems to a new set of technologies. But more must be done to raise awareness of AI and tackling the myths and uncertainty that exist around it. Only by doing so can we encourage greater adoption of AI in AML and reap the benefits of greater efficiency, effectiveness, and a more united front against financial crime.
To find out more, read Banking Circle’s whitepaper: Better by design? Re-thinking AML for a digital age.


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