Q&A with Andrew Davies, Vice President, Global Market Strategy, Financial Crime Risk Management at Fiserv

Although it sometimes can be overlooked as a serious crime, money laundering is a thorn in the side of financial institutions, businesses and regulators. Not only is money laundering a prevalent and pervasive issue in the financial sector, it can hide crimes that are far worse.

Financial institutions, businesses and regulators have all been working together to improve anti-money laundering efforts, and new approaches to technology and collaboration are fuelling the effort.

Andrew Davies, Vice President of Global Market Strategy, Financial Crime Risk Management at Fiserv speaks to Finance Digest about how financial institutions can leverage technology, data, and collaboration to aid in the fight against money laundering and associated crimes.

In a world where new financial crimes emerge every day, why does money laundering merit a focus?

While TV shows and movies can make money laundering seem glamourous, the predicate crimes that create the need for it are often particularly nefarious, including human trafficking and arms dealing. And we aren’t doing enough to stop them. According to the United Nations, around 2-5 percent of global GDP is laundered through the financial system annually – that’s somewhere between $800 billion and $2 trillion  – yet it’s estimated that as an industry we only stop 0.1 or 0.2 percent of the money that’s laundered.

Last year at the Financial Action Task Force (FATF) on money laundering’s plenary session, they created the slogan: “Stop money laundering, save lives”, and this captures the highest intent of AML. Simply put, if we can stop money laundering, we can stop the flow of ‘dirty’ money into the legitimate financial system, making it much more difficult for criminals to carry out a broad range of crimes.

Tell us more about how the financial industry is collaborating in the fight against money laundering.

There’s been a groundswell of interest and participation in collaborative efforts to fight money laundering. This is happening through a trinity of vendors, financial institutions, and regulators. What we’ve seen in the last 18 months is increased collaboration where the regulators are being more pragmatic about what they’re expecting from financial institutions, such as sharing information and leveraging different types of technology, practices that have a material impact on reducing the sum of money laundered through the system.

This is a very encouraging trend. If we share more information, we have more data, and that drives more effective use of analytics to stop money laundering. So that’s using, for example, machine learning (ML) and artificial intelligence (AI), and of course, the fuel that drives these technologies data.

One trend we’ve seen this past year has been in response to the increase in financial crime during the pandemic. More money stolen through online scams is being laundered through the financial system, and this has led several organisations, such as RedCompass Labs, to come together and compile typologies, algorithms, and red flags that financial institutions should look for as they try and detect money laundering.

How do AML processes benefit from automation?

ML, AI and intelligent automation (IA) can be applied across the entire AML lifecycle, including onboarding, monitoring, financial crime detection, case management and reporting. A particular focus has been on the detection of unusual activity to prevent money laundering and fraud and reduce risk. ML-based models are being used to identify red flags in data related to bank transactions and customer behaviour, and those same algorithms and techniques can be used for risk modelling.

For example, as a new customer is onboarded, what is the inherent financial crime risk? What indicators are there in their information that might show a higher risk of them being involved in money laundering or any criminal activity? This is evaluated during initial due diligence in onboarding a new customer, but it’s also done on an ongoing basis. If your customer base includes small businesses, the number of changes in control and ownership is likely high. So it is essential to continue due diligence across the entire lifecycle.

When it comes to detection, suspicious activity alerts can be combined with behavioural analysis from previous investigations to identify the alerts most likely to be connected to money laundering activity, enabling analysts to prioritize investigations.

Is it possible for smaller, more regional institutions to deploy the same capabilities and realize the same benefits as the larger institutions?

That’s an interesting question that applies to mid-tier institutions too – smaller and mid-tier banks don’t necessarily have the same volume of data that big banks have. When it comes to ML, the fuel for the technology is data, so this is where collaboration and access to shared learnings can play a big role. At Fiserv we have built a library of red flags indicators that we see across the entire market, and we make that available to each and every one of our clients.

Obviously, this sharing is subject to our clients’ permission, but the benefit is significant. It is important to share these typologies because criminals often take the path of least resistance. If they see a gap where an institution doesn’t have the same capability, they’ll try to take advantage of that. So we can share data insights through our technology via a library of red flags to an institution of any size, and everyone can learn from each other.