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5 anti-money laundering (AML) trends for 2021
Published by linker 5
Posted on December 11, 2020
6 min readLast updated: January 21, 2026
Global Banking & Finance Review®
Company
Published by linker 5
Posted on December 11, 2020
6 min readLast updated: January 21, 2026
By Justin Bercich, Head of Artificial Intelligence at Lucinity.
With the US making moves to lower the suspicious transactions threshold from $3,000 to $250 – meaning any transaction above $250 will have to be investigated manually – we will likely see banks forced into significantly increasing their operations to cope.
Banks are being squeezed from every angle. The COVID-19 pandemic has mounted even more pressure on compliance teams to become more efficient, as banks seek to quickly mitigate the financial impact of the pandemic and working from home. This, of course, comes at the cost of effective customer due diligence, as compliance teams fight to keep up with increased volume of less familiar cases – while pressured for cost and stuck with outdated technology.
With the transaction threshold also lowered, banks that rely on a rule-based approach will have to investigate extremely large numbers of transactions manually. This takes time and money, and human error will be an ever-present risk.
However, in 2021, we should hopefully see more banks and financial institutions adopting a risk-based approach, where transactions are flagged not purely based on an arbitrary amount, but on contextual, up-to-date information. At this time, only a Human AI approach meets these needs: to intelligently track and analyse this information and to flag transactions based on how suspicious it is within its context. Augmenting the skills and knowledge of financial crimefighters with AI designed to help them, will be crucial for success in 2021.
With customer attitudes and behaviours changing – not least COVID prompting an uptake in digital banking services – financial criminals will evolve their techniques in parallel. For example, the rise of virtual currencies in second tier risk jurisdictions – such as Eastern Europe and parts of the Middle East – will prove a breeding ground for money launderers, who can more easily slip into the financial system and remain hidden.
And, with tighter restriction around transactions, financial criminals will inevitably shift their focus elsewhere, potentially towards crypto and digital currency. Next year will see a lot of regulatory effort channelled towards virtual assets, custodian wallets, fiat currencies, pre-paid cards and the relevant KYC requirements for these areas.
These new entry points will confound traditional rule-based systems, which are unable to evolve and adapt, especially to new and complex scenarios. Thankfully, AI and machine learning becoming more common AML tools, meaning banks will no longer have to rely on what a human thinks a money launderer will do, but what an intelligent machine knows they do, based on historical behaviour, pre-analysed patterns and anomaly detection. For example, AI can detect anomalies and identify new placement schemes, and then cluster certain threat actors around them.
Fortunately, regulators understand this, which is why they’re attempting to foster a greater culture of transparency, so that all of the innocent victims of the money-laundering process gain visibility on the entire illicit flow.
One such tactic is more transparency around Ultimate Beneficial Owners (UBO), with support for UBO registers continue to pick up pace over the coming months. With banks, regulators, and governments possessing sharper visibility on the ultimate beneficiary of a financial transaction, they’ll be able to enact better customer due diligence checks to reduce fraud and money laundering activity.
However, not all parties are keen on the concept, with countries such as Switzerland, and the Cayman Islands not currently planning to adopt UBO registers. Next year, we’d predict these countries will experience an increase in illicit activity as a result, as financial criminals exploit ease of access to shell companies. Tensions will invariably rise between governments embracing UBO laws, and those who reject it.
While a call to arms for higher levels of collaboration and innovation in AML circles has been building over the past few years, new regulations will see this finally come to fruition during 2021-22.
The European Union, in particular, is making strides, with the EU Commission looking set to become the AML watchdog for the entire EU through the creation of EU wide AML institutions and supervisors. This will unify the EU in its pursuit of defeating money laundering with a more holistic and transparent approach across all member states. The EU is currently in its softer ‘directive’ phase of money laundering legislation, but the next two years will see these recommendations evolve into hard, compulsory law.
The key here is recognising that money laundering is a global, cross-border phenomenon. No single country, or even collection of countries like the EU, can shoulder the full responsibility for fighting money laundering. There must be increased knowledge-sharing between all casualties, from the private sector to regulators, and banks to governments. Only then can we truly understand the money laundering flow from start to finish.
With the COVID-19 pandemic relegating entire compliance teams to their homes, compliance officers must overcome the hurdle of managing thousands of people working remotely. After all, much of a bank’s security is housed within its physical walls, so setting up a remote and holistic digital infrastructure to meet a bank’s compliance needs poses a huge challenge.
Moving forwards, the rulebook essentially has to be completely ripped up. Banks need to properly interrogate the information design of their business’s continuity, applications must be overhauled with an entire remote workforce in mind. These cultural shifts can’t be achieved overnight though, banks just aren’t set up that way. It’s a gradual evolution, but one that must be started now, if it hasn’t already.
Again, artificial intelligence is an enabler here. As AI technology becomes more human, specifically when related to money laundering, the need for human confirmation becomes less pressing. Human AI will empower, not replace – training human investigators to react more efficiently and speeding up many aspects of AML, not least customer due diligence, which represents the lion’s share of money laundering cases.
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