3 min read
Use of AI and RegTech to Detect and Mitigate Money Laundering through Layering in the Brokerage Industry
Sankalpa Das and Nishank Bhatia
:
July 10, 2025
Sankalpa Das is an Associate Analyst II, and Nishank Bhatia is a Senior Manager, Client Delivery
at AML RightSource. We encourage our team members to share their insights
and expertise by contributing to our content library.
Overview
Money laundering is a global challenge, with the United Nations Office on Drugs and Crime (UNODC)1 estimating that between 2% and 5% of global GDP - roughly USD 800 billion to USD 2 trillion - is laundered annually. Money laundering has three stages: Placement, Layering, and Integration. Layering is particularly critical, as it involves the deliberate movement and transformation of illicit funds to obscure their origin, making detection and tracing more difficult for financial institutions and regulators.
Brokerage accounts enable the execution and transfer of various financial instruments, which increases the possibility of them being used for layering activities. Given the immense volume of trades conducted daily across global equity markets, these platforms create a high-velocity, high-volume setting that could be leveraged to obscure unlawful financial movements.
Traditional Brokerage and Cryptocurrency Brokerage-Based Money Laundering
Layering is the second and most sophisticated stage in the money laundering cycle that typically includes:
- converting cash into monetary instruments such as securities or crypto assets
- making frequent transfers between multiple bank or brokerage accounts
- executing high-volume trading with no economic rationale
- using shell companies or third-party intermediaries to mask ownership.
While traditional brokerage accounts remain a primary vehicle for money laundering activities, the exponential growth of cryptocurrency markets has introduced another avenue for layering illicit funds. Cryptocurrency brokerages and exchanges, whether regulated or unregulated, offer criminals the ability to move vast sums across borders almost instantaneously and often pseudonymously. According to Chainalysis' 2024 Crypto Crime Report(2), addresses linked to illicit activity received approximately $40.9 billion in cryptocurrency, with projections suggesting this figure may rise to $51 billion as further analysis identifies additional wallets and transactions. Although major crypto platforms (viz. Coinbase or Kraken) comply with AML regulations from bodies like FinCEN and the SEC, many offshore exchanges lack equivalent scrutiny.
Red Flags for AML Professionals, Regulatory Innovations, and Institutional Responses
Detecting signs of layering requires heightened vigilance conducted either through regulated brokerage platforms or decentralized cryptocurrency tools. Early indicators of suspicious activity:
- Layering activities remain below AML threshold alerts
- Masked by seemingly legitimate trading patterns with underlying techniques consisting of ATO fraud, mule recruitment, and multi-point fund dispersal
- Global laundering operation fuelled by digital anonymity and human deception.
Without a centralized governing body, the layering process unfolds outside the purview of financial regulators and often avoids triggering Suspicious Activity Reports (SARs). Hence, surveillance protocols are introduced as demonstrated below.
- ESMA (EU)3 reinforced MiFID II enforcement and emphasized the necessity of integrated data lineage tools to trace order origination and execution.
- SEC (U.S.)4, via its modernization of Rule 17a-4, now requires broker-dealers to implement WORM (Write Once, Read Many) compliant data storage.
- FCA (UK)5 launched its Smarter Regulatory Reporting (SRR) initiative. UK brokerages are implementing adaptive AI models, natural language processing (NLP)-based alert prioritization, and automated SAR drafting
This marks a transition from traditional, after-the-fact audit mechanisms to forward-looking, intelligence-driven monitoring frameworks that integrate machine learning, behavioural analytics, and regulatory technology (RegTech) to boost surveillance capabilities to deter layering activity across both traditional and digital platforms.
The Role of AI and Large Language Models in Modern Surveillance
Artificial Intelligence (AI) is rapidly transforming the surveillance landscape across brokerage and cryptocurrency platforms. In 2024, financial institutions globally invested over $3.6 billion in AI-driven compliance technologies, according to a report by Statista, signalling a strategic shift from manual monitoring to intelligent automation. AI-based systems now process and analyse up to 95% of trade data in real-time, using machine learning (ML) and graph analytics to detect layering schemes such as circular trading, spoofing, and account structuring. These tools could identify subtle anomalies, like unusually timed trades or cross-account fund movements.
One major innovation driving this shift is the development of tools that understand and respond to human queries, often known as Large Language Models (LLMs). These tools can quickly gather and interpret scattered data, ranging from identity records to past communications, without the need to manually run through each file. A 2024 McKinsey survey6 found that 43% of leading financial institutions reported at least a 30% improvement in alert triage efficiency after integrating LLMs into their AML operations. Suspicious Activity Reports (SARs) could be drafted using these models, and contextualizing account behaviour by processing both structured and unstructured data sources.
Final Insights
While brokerage and cryptocurrency platforms are vital components of today’s financial ecosystem, they remain susceptible to misuse by actors aiming to route unlawful funds through complex layering methods.
This article highlights the urgent need for smarter surveillance technologies, enhanced global regulatory coordination, and compliance systems that seamlessly integrate with emerging technologies.
Innovations such as AI and LLMs reduce manual work by automating tasks such as tracking regulatory changes or transaction monitoring with greater accuracy. They also speed up data handling and analysis—essentially doing things better rather than faster.
Sources:
- United Nations Office on Drugs and Crime. (n.d.). Money-laundering and globalization. https://www.unodc.org/unodc/en/money-laundering/globalization.html
- Chainalysis: https://www.chainalysis.com/blog/2025-crypto-crime-report-introduction/?utm_source
- ESMA: https://www.esma.europa.eu/sites/default/files/2025-04/ESMA12-1209242288-856_Report_on_Quality_and_Use_of_Data_2024.pdf
https://www.esma.europa.eu/press-news/esma-news/esma-report-shows-increased-data-use-across-eu-and-first-effects-reporting
- SEC: https://www.archive360.com/blog/sec-rule-17a-4-amended-taking-the-worm-requirement-out-of-our-misery
https://www.sidley.com/en/insights/newsupdates/2022/10/sec-modernizes-broker-dealer-recordkeeping-requirements