Leveraging Graph Analytics to fight Money Laundering

It’s estimated that money laundering (ML) transactions account for anywhere between 2% to 5% of global GDP. This amounts to roughly $2 trillion per annum.

Today’s struggle against ML brings with it hefty fines to institutions found to have breached anti-money laundering (AML) regulations. Two giant cases were recently settled involving two banking conglomerates. Back in 2018, US Bancorp and the Commonwealth Bank of Australia were fined a total of over $1 billion between them for careless AML control and non-compliance offences respectively.

This ruling showed that no institution, no matter how big, will be afforded any leniency and that nobody is above the law when it comes to AML. Therefore, organisations need an effective, yet simple to implement AML system in place. But what are the challenges that impede these systems from coming into play?

Challenges to AML compliance

  • False positives – on average, 95% of transactions flagged as potential ML turn out to be false positives. That is a huge number of erroneous flags, resulting in more serious and potentially real ML attempts being missed.

  • Criminal technology – nowadays, criminals are getting more and more sophisticated in their attempts to launder dirty money. Technology has its downsides in that it is making things easier for lawbreakers to hide behind false names and complicated web identities.

  • Manual time-consuming investigations – turning a flagged item into a Suspicious Activity Report (SAR) takes between 20 minutes to a few hours to complete. This is because it takes time to dig into the multitude of records and networks which make each transaction possible. Given the huge manual time, this process is expensive and inefficient.

Luckily there are solutions which help in easing these burdens.

Graph Solutions for AML Compliance

Relational databases have until recently proved to be well equipped to support transaction monitoring and performing basic analytics. However, their one major limitation is not being able to identify relationships between datasets, which is an essential element when following money trails and assessing risks.

Fortunately, graph databases can perform all the above tasks – they can analyse relationships to reveal hidden networks and clusters which may be symptomatic of illegal activity. The way these databases work is rather unique in that they store data in nodes (customers and entities along with their attributes), thus creating connections.

For AML purposes, the money flow trails and parties forming part of ML transactions create a web of relationships. These relationships are created via the three phases of ML which are placements, layering, and integration.

Graph databases are a big help to the fight against ML. They bring fragmented pieces of AML data and combine them to create the story of a particular transaction or money movement including the means and systems used to process the money.

The future of AML compliance

A key aspect of combating money laundering activities is the clear representation of transactions in a manner which enables the understanding of the movement of the money in question through a number of accounts. These could potentially include several intermediaries. Graph analytics can also capture the means used to interact with the relevant accounts which can be utilised to further study the money transfer behaviour.

Graph analytics offers a unique solution to banking and financial services when it comes to AML efforts. There is a good argument to be made that this new technology will become the industry leader in the not too distant future and that it will be the expected and preferred technology by regulators.

An increasing number of financial service providers are investing in graph analytics for AML monitoring and compliance. They are recognising the fact that modern technology has turned the cumbersome AML compliance process into an advantage for their organisation. So, stay ahead of the pack and adopt this new technology now rather than tomorrow. Contact us to learn how ComplyRadar utilises a graph database, providing you with the power of true analytics to inspect and act on suspicious transactions in real-time.

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