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Unravelling the web of criminal activity

Stemming the tide of money laundering might feel like an impossible task for law enforcement agencies when you consider just how pervasive it is. The UN estimates that between $800billion and $2trillion – the equivalent of 2-5 per cent of the world's GDP – is laundered through worldwide networks. These are the proceeds from serious organised crime, which has been named by the UK’s National Crime Agency as one of the deadliest threats we face. 

Since the war in Ukraine began, the UK has stepped up its efforts to identify the ‘dark money’ of Russian oligarchs, and the European Commission introduced a raft of new AML regulations last summer.

Criminals tend to follow a well-trodden path that involves moving funds from direct association with a crime (placement), disguising the trail (layering) and making the funds available from what appears to be a legitimate source (integration). Then they repeat the process, dividing large sums into multiple bank accounts to avoid arousing suspicion. 

Banks are, of course, on the front line in the fight against financial crime but they can only be successful if they’re armed with the right weapons. Technology has significantly increased the scale and speed at which criminals can move money between different accounts and shell companies, in multiple territories and currencies including crypto. 

Banks have invested heavily in transaction monitoring systems and compliance processes over several years, providing end-to-end AML monitoring capabilities. Yet, it is possible to enhance existing solutions through use of artificial intelligence (AI) and machine learning (ML) to identify patterns and emerging threats across an increasingly complex web of interactions and transactions. The technology can automate the processing of large data volumes, helping to identify subtle changes and allowing updates to existing systems or deployment of new monitoring scenarios.

With AI and ML techniques providing greater visibility into the data, the banks can adapt and deploy risk and scenario models far quicker than before. Running scenario simulations in rapid succession means that different monitoring strategies are validated, optimised as required and then deployed. Integration with wider systems is also possible – for example running a perpetual KYC process as changes within underlying transactional behaviours can trigger an automated update to assess any underlying changes in risk profile. 

All this is critical given how the threat level can rise, and regulations tighten, as we’ve seen during the Ukraine conflict. Firms want to avoid the financial and reputational costs of an FCA review, but there’s a clear ethical incentive for using AI/ML to improve compliance processes too. 

When applying AI/ML technology well, operational efficiencies can be achieved through reduced workloads for compliance teams, and it optimises customer experiences with faster onboarding and a reduction in the number of false positives. It’s certainly no coincidence that, according to our research, 53% of traditional banks see AI/ML as a way to gain a competitive advantage. 

While there’s no doubt AI/ML are beneficial, and there’s an urgent need to bolster AML processes, the challenge is implementing them across a large organisation, with a lot of legacy systems, due to the resources and time required. 

It’s therefore reassuring that growing numbers of banks are willing to lean on third-party providers to help them digitalise faster. Our research found that 69% already outsource AML processes, while 15% use both in-house and external services. However, the proportion willing to outsource AML is expected to rise to 81% over the next three years, while 12% will use a mix of the two. 

A low or no-code analytics platform empowers more people in an organisation to get the most from their data but vendors, with knowledge of financial services, also have a key role to play in delivering best practice, along with agile ways to build new capabilities and methods to interact with existing data and systems seamlessly. 

At SAS, we’re working with the European Banking Federation (EBF) to educate members about how they can tackle money laundering more effectively. Given that the organisation consists of 32 national banking associations from around Europe, representing nearly 6,000 banks with some 2.6 million employees, we’re hopeful that this will have a real impact.

There is no single solution in the fight against money laundering but advanced technologies, underpinned by training and ongoing support, could transform compliance processes and help banks close in on the perpetrators. 

Ends

Research taken from Banking on Technology, SAS (2022)

 

 

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This post is from a series of posts in the group:

Artificial Intelligence and Financial Services

Artificial Intelligence and Financial Services


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