Michael Down, Head of Financial Services Technology, Neo4j
Money laundering is a massive global issue, with the UN estimating that between 2% and 5%, 603 billion to 1.75 trillion Pounds, of the world’s GDP is laundered annually. In the UK alone, over a quarter of crimes are linked to illegal financial activities. With the UK government looking to have the Financial Conduct Authority (FCA) absorb the Payment Systems Regulator (PSR) and take over its functions – in efforts to save short-term costs – the strain on resources to catch these crimes is only set to increase. In parallel, the European Union’s Digital Operational Resilience Act (DORA) emphasises the drive to strengthen digital operational defences throughout the financial sector. The DORA framework addresses such vulnerabilities by guiding financial entities towards adopting robust information and communication technology security and risk management standards that criminals might otherwise exploit in their money laundering endeavours.
This shift comes at a time when payment scams and payment technologies are evolving fast, heightening concerns around money laundering and financial crime. And while the PSR and FCA have shared functions since the PSR’s inception, questions remain about how effective this consolidated approach will be in combatting criminal activity. Adding further weight, in January the FCA published an updated analysis on money laundering through the markets, highlighting the risk of capital markets being misused to more illicit funds under the guise of legitimate transactions.
In response to these challenges, financial organisations are increasingly turning to emerging solutions, such as AI and graph databases, to delve deeper into suspicious activities. With money laundering tactics evolving, it’s crucial for the industry to adopt fresh approaches that can keep pace.
Money laundering in the real world – and why it’s hard to bust
What makes money laundering so challenging to detect is that criminal activities are often hidden in plain sight, masquerading as legitimate and mundane. With so much at stake, criminals have become adept at creating intricate networks of identities, shell companies, and banking accounts to obscure their activities. These multiple layers can look entirely innocuous on the surface, which is precisely the problem for traditional anti-money laundering (AML) systems.
Conventional AML measures typically look to identify deviations from standard patterns within discrete data and transactions. At a technical level, they are based on a relational database model where data is stored in rigid tables and columns. The underlying assumption is that ‘normal’ activity can be measured against an outlier, yet if a criminal has always operated illegally, their behaviour might not register as unusual.
This approach is often ill-equipped to handle vast data sets of financial records, and it struggles to pinpoint the patterns that might indicate a hidden network of illegal transactions. The result is a deluge of false positives, with investigators spending too much time on fruitless leads. Meanwhile, genuine threats can slip past undetected if the transactions in question appear superficially consistent with the criminal’s established history.
Using AI and graph databases to beat the launderers
Where many relational database AML solutions fall is their inability to apply broader contextual insight. What is needed is the ability to follow a trail from one account to another; a 360-degree view of complex money laundering networks is necessary to flag connections between assets and individuals – and is something that knowledge graphs can provide.
Financial institutions often have huge blind spots regarding transactional fraud, because criminals spread their activities across various accounts or even different financial providers. A launderer rarely sends money directly from one bank to another in a linear path; rather, they route through a sophisticated web of ‘mule’ accounts. This makes it difficult for any single institution to gain full visibility into the end-to-end flow of money.
Graph database technology, in particular, is well-suited for AML efforts, as any number of qualitative or quantitative properties can be assigned to data, describing complex patterns coherently and descriptively. Graph databases use individual data such as ‘person,’ ‘account,’ ‘company,’ and ‘address,’ along with their connections to one another, such as ‘registered at’ or ‘transacted with,’ to uncover complex connections. Specifically, looking for key individual data and connections in this way means financial institutions can uncover intricate networks quickly, flagging suspicious connections that would otherwise remain hidden. A notable example of this is how the International Consortium of Investigative Journalists (ICIJ) used Neo4j to sift through millions of leaked files in the Panama Papers investigation, quickly making out hidden offshore structures and exposing cross-border links between individuals and assets. This same approach gives today’s financial institutions a powerful edge in spotting suspicious connections that might otherwise remain buried.
Modern technologies busting age-old crimes
Graph database software and AI technology are moving AML investigations to a granular level. Real-time analysis that uncovers data patterns is the only way to keep one step ahead of the criminal networks and their dirty money. Armed with graph database software and AI, financial institutions can take on the money launderers and win.
The FCA’s push for continuous review of systems and controls, as noted in its latest analysis, dovetails with this technological advance. Public bodies and private firms alike must innovate and align to tackle the evolving threat of money laundering, and the government’s plan to merge the PSR with the FCA reinforces the momentum behind a more unified regulatory stance.
By harnessing graph databases and AI, the banking and finance sector is better equipped to detect and disrupt illicit schemes before they gain ground. As these modern technologies become increasingly embedded in AML workflows, money launderers will find that staying under the radar is no longer as simple as shuffling funds through a patchwork of seemingly unrelated accounts. The result is a financial system that is more resilient and less hospitable to criminals, and that’s precisely the outcome regulators and legitimate market participants are working to achieve.
