Published by Jessica Weisman-Pitts
Posted on April 4, 2022
4 min readLast updated: February 8, 2026
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By Glenn Smith, AML & Fraud Customer Advisory UK & I, SAS
The UN agency against crime and drugs estimates that between 800 billion and 2 trillion dollars are laundered through worldwide financial systems each year, which is an astonishing 2 – 5 per cent of the world’s GDP. Tackling this problem is therefore a key focus for financial institutions, with the spotlight recently turning to how artificial intelligence (AI) and machine learning can boost effectiveness and curb the deluge.
Typically, the money laundering process will follow three stages:
● Placement – the moving of funds from direct association with a crime.
● Layering – disguising the trail.
● Integration – making the funds available to the criminal from what appears to be a legitimate source.
Often the stages are combined or repeated, with a large sum divided into several smaller sums and then deposited in different accounts.
However, this isn’t the only challenge. Criminal networks are becoming far more sophisticated, and able to deploy new technology at speed in an attempt to avoid many of the conventional anti-money laundering (AML) checks. Not only has this added to the complexity of the AML process, it has also forced the banking community to accept a new reality in the fight against financial crime, with AI and machine learning playing a vital role to quickly uncover emerging, complex money laundering and terrorist financing threats and reveal hidden relationships.
Economic sanctions remain a key area of focus in the fight against financial crime, reinforcing the interconnectedness between sanctions, know your customer (KYC) and AML compliance which is needed in every customer related interaction and transaction. This is why having an end-to-end financial crime capability is critical.
The industry is at a critical crossroads
Taking into account new and emerging challenges linked to technological innovation, the European Commission introduced a new AML package last summer, setting out a number of regulations banks and other obligated entities needed to comply with. The main aim of the package was to close the loopholes used by criminals to launder illicit proceeds, as well as improving the detection of suspicious transactions and activities.
Many experts believe that this action couldn’t have come soon enough, with the Financial Action Task Force (FATF), the intergovernmental watchdog for financial crime, warning in January 2021 that the COVID-19 pandemic had enabled criminals to exploit digital identity loopholes.
Around the same time, SAS, KPMG and the Association of Certified Anti-Money Laundering Specialists (ACAMS) surveyed more than 850 ACAMS members to gauge how compliance professionals were adjusting their AML strategies amid the pandemic.
The results found that one-third of financial institutions had accelerated their AI and machine learning adoption for AML compliance in response to COVID-19, while another 39 per cent indicated their AI and machine learning adoption plans continued unabated, despite the pandemic’s disruption.
What this research shows is that the industry is at a critical crossroad. Banks now recognise cloud-based AI as an AML game changer, yet some are still unsure how to harness the technology in the most effective manner. SAS recently partnered with the European Banking Federation (EBF), an organisation comprising 32 national banking associations from across Europe, representing nearly 6,000 (5,981) banks and roughly 2.6 million employees to provide education around how the fight against money laundering can be better managed.
Our aim is to highlight how with the right approach, the deployment of AI and machine learning across the AML sector can deliver significant change, helping banks to better identify, track and prevent illegal activity.
This is made possible through a common analytics platform and module-based solutions for enterprise fraud, customer due diligence, anti-money laundering and enterprise case management.
Click here to find out more about the virtual series of masterclasses, designed to bring together AML experts to explore the most advanced technology solutions for anti-money laundering.
Money laundering is the process of making illegally obtained money appear legitimate. It typically involves three stages: placement, layering, and integration.
Anti-money laundering (AML) refers to laws, regulations, and procedures aimed at preventing criminals from disguising illegally obtained funds as legitimate income.
Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems, which can be used to analyze data and detect patterns.
Compliance in banking refers to the adherence to laws, regulations, and guidelines that govern financial institutions to prevent illegal activities such as money laundering.
The European Banking Federation (EBF) represents the banking sector in Europe, advocating for the interests of banks and promoting best practices in the industry.
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