Sophisticated criminal enterprises, like the international money-laundering organization that was charged in April 2025 for moving millions in drug proceeds through major banking systems, can systematically exploit trade-based money-laundering vulnerabilities. Anurag Jain of Van Dermyden Makus explains why traditional AML defenses fail against coordinated global networks and recommends steps for compliance professionals to detect these schemes.
Despite the recent federal charges against them, the international money-laundering organization that moved $30 million in drug proceeds through major banking systems operated with precision across three continents. Cash couriers collected drug proceeds from distribution points in Miami, Houston and Los Angeles, delivering funds to export companies registered in Georgia and Texas. These exporters purchased electronics, including smartphones, tablets and computer components, then shipped them to distributors in China and the Middle East while banks processed the payments as legitimate commercial transactions.
The criminal enterprise structured its operations to exploit specific weaknesses in traditional monitoring. Teams deposited cash proceeds in amounts just below $10,000 across multiple bank branches, ensuring individual transactions never triggered currency transaction reports. Export companies then used these funds to purchase goods at significantly inflated prices — paying $800 for smartphones worth $300 — transferring the price differential to overseas accomplices.
Chinese money-laundering brokers coordinated the overseas component, converting exported goods into local currency and distributing proceeds to cartel representatives. The brokers maintained networks of legitimate businesses that purchased the electronics at market prices, effectively laundering the markup through established commercial channels.
The scheme succeeded because each component appeared legitimate within traditional risk assessment frameworks. Cash deposits stayed below reporting thresholds, export transactions included proper documentation, and goods moved through established shipping channels. Only when investigators combined customs records, bank transactions, shipping manifests and location intelligence data from multiple agencies did the criminal enterprise become visible.
Critical challenges in TBML detection
Trade-based money laundering (TBML) schemes exploit fundamental gaps in how financial institutions monitor trade finance transactions. Dual-use goods create challenges because legitimate and criminal transactions involve identical products shipped through the same channels. Electronics, precious metals and luxury goods serve both lawful commerce and money-laundering operations, making transaction-level detection extremely difficult.
Price manipulation represents another detection challenge. Criminal organizations systematically over-invoice exports and under-invoice imports to transfer value across borders. A smartphone invoiced at $800 instead of its $300 market value transfers $500 in criminal proceeds, but banks typically lack real-time pricing databases to identify these discrepancies during transaction processing.
Due-diligence gaps compound these challenges further. Trade finance documentation requirements focus on transaction mechanics like letters of credit, bills of lading and shipping manifests, rather than underlying economic substance. Banks verify that paperwork is complete and properly executed but rarely validate whether cargo values match market prices or whether trading relationships make commercial sense.
Disconnected data assets create more institutional blind spots. Import/export transactions, cash management activities and wire transfer monitoring operate in separate systems with limited cross-referencing capabilities. In this instance, the network’s cash deposits in Florida and export payments in Georgia remained invisible as connected activities until law enforcement manually correlated the data.
Alert resolution delays can provide additional criminal advantages. Traditional investigation processes require analysts to manually gather information like transaction histories, shipping records, corporate registrations and regulatory filings that exist across multiple systems. This process often takes weeks, allowing criminal networks to complete their schemes and disappear before investigations conclude.
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Read moreDetailsTransformation with AI and machine learning
The detection challenges outlined above — including disconnected data systems, delayed alert resolution and complex price manipulation schemes — require solutions that match the sophistication of modern criminal enterprises. Artificial intelligence (AI) and machine learning (ML) applications can effectively address these longstanding vulnerabilities.
Large language models (LLMs) can automatically analyze vast amounts of unstructured data, such as shipping documents, corporate filings, news articles and regulatory reports. With this information, AI can compile comprehensive intelligence packages, including corporate ownership structures, shipping histories and related suspicious activity reports. By handling labor-intensive document review and extracting key information like consignee relationships, shipping route anomalies and documentary inconsistencies, LLMs reduce investigation time from weeks to hours while freeing investigators to focus on strategic analysis and decision-making rather than manual data gathering.
Complementing these models, machine learning models trained on historical pricing data can identify over- and under-invoicing in real-time. These models analyze commodity prices, seasonal variations, supplier relationships and geographic factors to establish expected price ranges for specific goods. When transaction values fall outside normal parameters, the system generates immediate alerts for specialized review.
Similarly, relationship-mapping algorithms can expose complex patterns that manual investigation would miss. By analyzing customer connections, shipping routes, payment flows and communication patterns, these systems identify coordinated criminal enterprises operating across multiple institutions and jurisdictions.
Behavioral analytics and natural-language processing close the loop. Behavioral analytics models learn normal patterns for specific industries and trading relationships, flagging deviations that suggest criminal activity. When established electronics exporters suddenly begin shipping to new destinations with unusual payment terms, or when cash-intensive businesses develop export operations inconsistent with their historical profiles, these models generate targeted alerts.
Natural language processing tools can analyze communications data like emails, text messages and shipping instructions to identify coded language and suspicious coordination between supposedly independent parties. These capabilities proved crucial in the April case, where investigators discovered that separate export companies were receiving identical shipping instructions from the same criminal coordinators.
Regulatory expectations intensify at midyear
The sophisticated schemes revealed in the April case have exposed critical gaps in institutional detection capabilities. More broadly, FinCEN analysis shows only 24% of financial institutions clearly identify TBML activity in suspicious activity reports, highlighting systemic weaknesses in detecting global money-laundering rings. Near halftime of 2025, regulators expect institutions to strengthen their programs by maintaining watchlists for known facilitators, shipping agents and communication channels from prior enforcement cases, with real-time screening against new customers and counterparties.
Supervisors increasingly demand evidence that institutions can break structuring rings quickly through advanced analytics. The expectation is that systems combine deposits under $10,000 made within 48-hour windows across different branches, using common identifiers like device IDs or even vehicle registration data, which are precisely the techniques the network used to circumvent traditional currency transaction report thresholds.
Trade finance due diligence has become a particular examination priority. Institutions must document how they verify an economic purpose behind bulk electronics exports commonly used in laundering schemes. This includes risk scoring based on commodity codes, third-party shipping data validation and invoice authenticity verification before processing associated wire transfers.
FinCEN’s 2024 proposed rule modernizing AML/CFT programs establishes explicit board-level oversight requirements for all financial institutions. The proposed rule requires that boards of directors or equivalent governing bodies approve and oversee financial institutions’ AML/CFT programs, with appropriate oversight measures including governance mechanisms, escalation procedures and reporting lines. This represents a significant shift from previous senior management approval standards to mandatory board-level accountability.
Supporting this governance framework, risk assessment formalization has become a cornerstone regulatory expectation. FinCEN now explicitly requires financial institutions to conduct periodic AML/CFT risk assessments that consider current AML/CFT priorities, business activities, customer profiles and suspicious activity report patterns. These assessments must be updated following material changes to institutional risk profiles and must demonstrate how detection systems address identified vulnerabilities, creating the foundation for board oversight and strategic decision-making.
Building on these foundational requirements, TBML-specific regulatory expectations have intensified considerably. FinCEN requires institutions to include “TBML” abbreviations in suspicious activity report narratives and provide explanations of why customers are suspected of participating in these schemes. The regulatory focus extends to shortening the intervals between when TBML activity occurs and when it gets reported. Analysis reveals some suspicious activity has only been identified five years after occurrence, highlighting the detection gaps that enhanced risk assessments must address.
To close these gaps, enhanced customer due diligence standards now encompass sophisticated criminal methodologies identified through risk assessments. Institutions must demonstrate capabilities to identify customers whose travel patterns suggest cash courier operations, requiring integration of location analytics and behavioral monitoring into risk assessment processes. Complementing these operational requirements, the proposed rules mandate that AML/CFT program personnel remain located in the US and be accessible to Treasury Department oversight, ensuring direct regulatory access to critical compliance functions that support both board governance and day-to-day detection activities.
Tying these elements together, model validation requirements have evolved beyond traditional testing to ensure the entire framework operates effectively. Regulators expect institutions to conduct annual exercises injecting known criminal methodologies, such as a cash-to-electronics operation, into synthetic datasets to validate detection system precision. Through these exercises, institutions must demonstrate they can identify coordinated criminal enterprises operating across multiple transaction types and geographic boundaries, providing boards with measurable evidence of program effectiveness while confirming risk assessments accurately capture emerging threats.
Aligning with regulatory expectations requires demonstrating measurable improvement in TBML detection capabilities. This means establishing baseline metrics for investigation time, alert resolution efficiency and criminal enterprise detection rates, then documenting systematic improvement through AI/ML implementation. Regulators increasingly expect evidence-based demonstrations of program effectiveness rather than process descriptions.
Adopting AI/ML and aligning expectations
Compliance professionals must recognize the critical need for integrated data platforms that combine cash management, trade finance and wire transfer monitoring. The success of the criminal enterprise is dependent on exploiting disconnections between these traditionally separate monitoring functions. Institutions should invest in real-time price validation and automated relationship mapping to uncover the coordinated tactics behind modern TBML schemes.
LLM-powered investigation tools that can rapidly analyze complex trade documentation and compile comprehensive intelligence packages are transforming investigation efficiency while improving analysis quality. Machine learning models for pricing anomaly detection offer immediate value, particularly for institutions with significant trade finance portfolios, and should also rank among an organization’s top implementation priorities.
Long-term strategic planning must emphasize comprehensive AI/ML integration across all AML functions. This includes behavioral analytics for unusual trading pattern detection, network analysis for criminal enterprise identification and natural language processing for communication analysis. These technologies represent fundamental shifts in detection capabilities rather than incremental improvements.
The aforementioned global network’s sophisticated operation reflects the current state of global money-laundering threats. As criminal enterprises continue evolving their tactics, institutions must embrace AI/ML-powered detection systems while aligning with regulatory expectations to maintain a defensive advantage. The choice facing compliance leaders is not whether to adopt these technologies, but how quickly they can implement them before the next sophisticated scheme exploits their vulnerabilities.
Anurag Jain is a financial crime compliance expert with a focus on investigations, regulatory compliance and the use of AI and technology in financial crime risk management. He provides investigative services at Van Dermyden Makus Law Corp. and leads compliance consulting through EntityVector. His practice spans fraud risk management, anti-money laundering (AML), Know Your Customer (KYC) and the application of emerging technologies to strengthen financial crime controls. 
