Anti Money Laundering with Graph Database

Digging in the Dirt – Spotting Money Laundering with Graph Databases

In 2020, Australia’s financial crime watchdog AUSTRAC hit the Westpac bank with the biggest penalty in Australia’s corporate history – $1.3 billion – for failing to spot and stop more than 23 million instances of money laundering.

While the pecuniary penalty itself is an eyewatering number, what’s more, astonishing is the enormous volume of transactions processed by Westpac that breached the Anti-Money Laundering and Counter-Terrorism Financing Act.

As was noted in AUSTRAC’s findings, the breaches included a failure by Westpac to properly report on more than 19.5 million ‘IFTIs’ (international funds transfer instructions) with a total value of over $11 billion.

It seems like it would be hard to miss 23 million occurrences of anything – but therein lies the challenge for spotting money laundering in today’s global digital economy.

One massive $23 million transaction – especially if obtained illegally – might raise suspicion if it was from a single, obviously problematic source, but 23 million online transactions of $1 through a variety of generic companies or entities, might just look like a lot of people transacting normal, everyday, genuine business.

So, how big is the problem, and what can be done about it?


The scale of the problem

Briefly, money laundering is defined as the illegal process through which ‘dirty money’, or money obtained by criminal activity (eg. drug trafficking, corruption, embezzlement or gambling) is made to look like it is coming from a legitimate source. And since the 1930s, when the first laws against money laundering were created during the Prohibition era in the USA, the problem has only grown worse.

In 2017, a report from PWC calculated that global money laundering transactions in the previous year made up approximately two to five percent of the global GDP, estimated somewhere between $1 to 2 trillion dollars. (Are your eyes watering again?)

In the years since, the costs of preventing money laundering have also soared, with the total projected cost of financial crime compliance globally rising 18% in the space of just one year to reach nearly $214 billion in 2020, according to one report.

Also, as the number of illegal transactions and their cumulative value has risen, so have the laws around preventing money laundering. Failing to do anything about the problem has seen the total of US and EU fines for banks that recorded misconduct, including anti-money laundering violations, exceed $342 billion as of September 2017.

Clearly the problem is not going away, but a new technology is emerging that can help fight it.


Scaling technology to meet the problem

The Australian Securities and Investments Commission (ASIC) provides a list of examples of suspicious activity that must be reported by financial institutions, as it may reveal instances of money laundering. These include:

  • unusual or unexpected trading activity
  • trading ahead of a price-sensitive announcement
  • transactions that make no economic sense
  • instructions to place an order immediately or urgently
  • orders inconsistent with previous behaviour or profile.

Additionally, financial institutions need to be able to identify any transactions that:

  • appears unusual
  • has no clear economic purpose
  • appears illegal
  • does not fit with the customer’s profile or business activities
  • involves proceeds from an unlawful activity, or
  • indicates that the customer is involved in money laundering or terrorism financing activities.

On the face of it, being able to spot and identify activity amidst the volume of transaction data that most banks deal with every day is obviously beyond the capability of one human brain – or even many humans working together, for that matter.

And while banks or other financial enterprises may have access to the data that could reveal illegal activity, they may not have the technology that can look at those millions of data points and link together those that have relationships that show evidence of money laundering.
This is where the technology of the Graph Database can provide a solution.


How Graph Databases find the dirt on money laundering

The Graph Database is a relatively new software tool for analysing financial data that is especially good at exploring and finding connections between millions of data points that can reveal the relationships and patterns between them.

In this way, the graph database becomes the ideal tool for digging through the dirt of financial datasets to dissect the flow of money, and so reveal those instances noted above which may be evidence of money laundering activity.

Graph databases work on finding common attributes and connections in the transactions of entities they analyse, such as addresses, phone numbers and IP addresses, and in so doing create a visual representation of the flow of funds through financial systems, which can then reveal them to be activities in one of the three phases of money laundering – placement, layering and integration.

Connections at a scale like these are simply too much for the human brain, with all its limitations, to make on its own. However, with human analysts working with graph databases that can sift through the millions of data points to uncover the deeper linked and not-so-obvious relationships, banks can much more easily report on and assess whether transactions are money laundering or genuine.

Westpac could certainly have benefitted from using graph databases in 2020 to identify money laundering activity in its systems. Your financial institution can benefit by starting to use graph databases today.


Enabling anti-money laundering reporting with TigerGraph

Intech Solutions utilises the powerful data analysis capability of TigerGraph for our anti-money laundering solutions. TigerGraph differentiates itself from other solutions through its ability to scale to review billions of data points in a day and perform the deepest link analytics possible, comfortably providing superior graph-assisted transaction monitoring for the largest financial institutions and payment networks on a real-time basis.


Why not get in touch with Intech Solutions today for a confidential discussion on how the IQ Office solution can deliver your business all these benefits of location intelligence, and more.

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Intech provides data solutions that lay a secure foundation for robust, cost-effective and timely business transformation. Intech’s products have been successfully deployed to thousands of users, across hundreds of sites. See

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