It is a complicated criminal process, but how can financial institutions keep up with the evolution of techniques in laundering? One challenge for compliance teams is layering—the process of disguising the origin of illegal funds. Former FinCEN director Jamal El-Hindi says, “Criminals will exploit any crevice in the system, and technology now gives them the means to move money more quickly and obscurely than ever before.”
The new tools are helping financial institutions stay a step ahead by processing more extensive data sets and identifying patterns that humans would probably miss. The article expresses how technology and big data are reshaping AML efforts on the ground in terms of detecting and preventing layering activities.
What is layering in money laundering?
Layering is one of the significant stages of the laundering process, which aims to distort illegally gained cash from its source through deception. At this phase of the layering process, criminals try to “layer” their money by moving it through different accounts and transactions.
By doing this, the possibility that the money will be traced back to any criminal acts, like drug trafficking or tax evasion, is reduced. It is through the layering process that bad actors can disperse assets and thereby make their funds appear clean in the practice of money laundering.
Compliance teams must leverage technology to cut through these intricate webs of movements and activity. Reports indicate that between $800 billion and $2 trillion get laundered annually worldwide, with a lot of schemes involving tailored techniques of layering.
The Role of Technology
Technology provides new ways in which layering in money laundering and layering AML efforts may be detected by financial institutions. Advanced tools let compliance professionals monitor, in real-time and in thorough detail, all clients’ transactions across all accounts.
They can analyze payment flows and transactions, which, on their own, may have needed more work to investigate. This identifies suspicious patterns or networks of activity that point out the layering of money laundering. By 2024, approximately 65% of the world’s leading banks would have adopted advanced analytics in a bid to spot complex money laundering.
Bonus: To learn more about how your financial institution can leverage leading technologies in the implementation of advanced layering detection capabilities to drive forward on AML compliance, please refer to amlwatcher.com or contact a representative.
Data Analytics Tools
The in-place tools of financial institutions provide robust data analytics, applying layers of context to the complex customer transaction data. The solutions can merge internal and external databases to build a full view of account holders.
This information linking entities, business relationships, and past financial behaviors aids in flagging attempts to disguise funds through multiple layering money laundering methods. Material changes in customary business practices or fund movements between unfamiliar parties generate alerts.
Transaction Monitoring Systems
In this respect, transaction monitoring systems are at the very heart of the detection of the various stages of money laundering, including layering. These systems extensively analyze the financial transactions of clients, raising a red flag immediately in case of any transactions that deviate from the expected patterns of behavior. This could be an indication that someone is trying to layer illegally gained assets between accounts in an attempt to obscure their source, which is common in layering AML schemes.
This is possible through the systems keeping track of the flows of funds in near real-time and raising alerts to investigators where they perceive suspicious movements between accounts or anomalies across related transactions, three layers of money laundering. Transaction monitoring technology is projected to prevent an estimated $150 billion worth of potential money laundering annually across the globe by 2024.
AI and Machine Learning
New machine learning algorithms can realize value in large volumes of complex transactions and client data by recognizing subtle patterns and interconnections that indicate possible layering of money laundering, which the human eye might miss. AI models learn from historical case data to identify the various stages of money laundering and then repetitively test incoming information to pinpoint similar activity.
It powers predictive capabilities that help compliance teams proactively head off attempted laundering before it’s complete. Recent reports indicate that the use of AI and machine learning has improved detection rates of suspicious activities by up to 40% in some financial institutions.
Pattern Recognition
Advances in pattern recognition now make it possible to discern the fragile financial fingerprints sometimes left behind by money laundering schemes. The software analyzes metadata from past investigations of layering AML activities to understand typical sequences within the stages of money laundering.
It picks up red flags and indicates repeat patterns that join entities or accounts involved at some stage of earlier laundering cycles. In this context, compliance officers can join the dots, which may otherwise have been too fine. Pattern recognition has evolved by 25% in 2024 to detect a more complex money laundering network across major banking platforms.
Alerts on Triggers
These systems generate an alert when they detect suspicious patterns or behaviors corresponding to the different stages of money laundering, identified by the transaction monitoring system or AI-driven analytics.
Practical triggers for alerts consider holistic context derived from both internal and external data sources. They underline transactions and relationships between entities, among other details, which are far enough out of expected normative activities to warrant investigation. Removing false positives allows teams to focus manual review time on the most high-risk alerts that are indicative of real threats of criminal laundering or non-compliance with AML standards.
