An exclusive article by Fred Kahn

Every year, hundreds of billions in illicit funds snake their way through the global trading system, hiding behind the complexity of cross-border commerce. Trade-Based Money Laundering (TBML) is notoriously hard to detect, yet advances in data-linkage are quietly redefining the boundaries of what’s possible in financial crime compliance. With a strategic approach to linking disparate data sets, compliance teams can finally see patterns, relationships, and red flags that previously slipped through the cracks. The opportunities are vast, and the results can be game-changing.

Data Linkage in TBML: Redefining the Compliance Battlefield

For decades, money launderers have exploited gaps between customs, shipping, finance, and corporate registries to mask illicit trade. The core issue is that the data needed to expose suspicious trade often sits in silos: shipping manifests on one platform, container IDs in another, supplier records locked in private registries, and payments tracked in yet another system. Data-linkage, the structured and systematic integration of these sources, is rapidly transforming compliance work.
By merging container data, transaction records, shipping logs, supplier profiles, and Ultimate Beneficial Ownership (UBO) registers, institutions can surface red flags impossible to spot otherwise. Regulations are finally catching up: FATF’s updated Recommendations 10, 13, and 24, the EU’s AMLA proposals, and the US AML Act all emphasize beneficial ownership, transparency, and data integration. Supervisors now expect financial institutions to not only monitor their customers, but also to understand transactional context, supply chain relationships, and even the physical flow of goods.

Consider a global commodities trader. Previously, a compliance analyst would only see invoices and payment details, with limited view of the actual cargo or parties behind the supplier. Now, by linking shipping container numbers to port call data and customs registries, the same analyst can spot anomalies—like reused or recycled container IDs—suggesting fraud or trade manipulation. In parallel, supplier details can be matched with global UBO registers, flagging connections to high-risk jurisdictions or previously sanctioned entities. By bridging these data islands, money laundering typologies that once seemed sophisticated become dramatically easier to detect.

Container Tracking and Number Validation: Exposing the Phantom Freight

One of the classic TBML schemes involves manipulating the physical movement of goods—fake shipments, ghost containers, or misdeclared cargo. Money launderers may reuse the same container number for different shipments or fabricate transit routes altogether. Linking official container number registries, satellite AIS (Automatic Identification System) vessel tracking, and port entry logs allows compliance teams to challenge the narrative presented in trade documentation.

Suppose a European bank receives an invoice and shipping documents for a consignment of electronics, container number ABCD123456, allegedly shipped from Shanghai to Hamburg. By cross-referencing this container number with global container registries and real-time vessel tracking, compliance officers can confirm whether the container ever left Shanghai, entered a vessel, and arrived at the stated port. If the number is linked to multiple shipments over the same timeframe, or if its physical location does not match the trade narrative, the likelihood of TBML or fraud escalates rapidly.

In addition to container IDs, data-linkage makes it possible to cross-reference the official identification number of the vessel transporting the cargo with external registries detailing vessel characteristics. For example, the International Maritime Organization (IMO) number assigned to each vessel can be matched with publicly available databases specifying the vessel type, size, and capabilities. If the trade documentation claims that a large volume of bulk commodities was shipped on a vessel registered as a small fishing boat or a type of ship unsuited for such cargo, this stark mismatch instantly raises suspicion and may reveal trade manipulation or fraudulent shipping activity.

Regulators are increasingly encouraging or requiring financial institutions and shipping companies to participate in such integrated data ecosystems. For example, the World Customs Organization (WCO) and INTERPOL have launched data-sharing initiatives to monitor container traffic, detect route manipulation, and spot containers with “impossible” travel patterns. By embedding container tracking in routine due diligence, compliance professionals can quickly flag recycled or “phantom” containers, reducing one of TBML’s most persistent vulnerabilities.

Ultimate Beneficial Ownership: Connecting the Dots Behind Suppliers

Trade finance is rife with shell companies and nominee entities whose true ownership is often deliberately obscured. Criminals use these veils to disguise the origin of funds, move value across borders, and hide beneficial owners from scrutiny. With the EU’s Fifth and Sixth AML Directives, and corresponding US and APAC regulations, transparency around beneficial ownership has become non-negotiable. Yet the data needed for real UBO analysis is rarely in one place.

Data-linkage empowers compliance teams to break through this opacity. By combining trade documentation, supplier registries, government corporate databases, and international UBO registers, institutions can piece together the web of ownership behind trading partners.
For example, if an importer based in Antwerp sources goods from “XYZ Holdings Ltd,” a compliance analyst can use linked data to trace that company through offshore company registers, reveal its ownership by a Panamanian trust, and cross-check for politically exposed persons or sanctioned entities. The recent implementation of the EU’s Beneficial Ownership Registers Interconnection System (BORIS) and the US Corporate Transparency Act are accelerating the availability of these datasets. When suppliers are connected to multiple entities in high-risk jurisdictions, or share beneficial owners with other flagged entities, the risk profile changes instantly.

Linked data can also detect circular trading or “round tripping”—where the same beneficial owners control multiple nodes in a trade chain, using trade flows to disguise illicit funds or create the illusion of genuine commerce. Real-time monitoring of UBO relationships has already helped European and Middle Eastern banks block transactions linked to major TBML cases, as confirmed by recent FATF and Europol advisories. The possibilities here are only beginning to be tapped.

Unmasking Over and Under-Invoicing with Smart Data Fusion

Manipulating the price or quantity of goods is one of the most common TBML techniques. Over-invoicing allows illicit funds to exit a country disguised as legitimate payments, while under-invoicing can repatriate proceeds or evade capital controls. Traditional transaction monitoring systems are often blind to such manipulation, focusing only on payment flows without context. Data-linkage can change the game.

By integrating customs data, market pricing databases, bills of lading, and historic trade records, compliance teams can automatically benchmark declared values against accepted market rates. For example, a transaction involving copper wire from Chile to Singapore can be assessed not just on invoice value, but compared with global spot prices, shipping costs, insurance rates, and prior trade records for the same supplier. If the declared value is dramatically above or below these benchmarks, automated alerts can be triggered for enhanced review.

Some governments are already piloting advanced data-linkage models. The Singapore Customs Authority now links import/export declarations to both shipping company data and global commodity exchanges, enabling detection of outlier pricing in real time. In Europe, several major banks are experimenting with vendor linkage to independent price indices, automatically flagging suspiciously priced transactions and identifying cases where declared cargo volume does not match shipping container capacity or historical trading patterns.

The ability to link trade, financial, and market data enables compliance teams to spot not just individual suspicious transactions, but systemic price manipulation patterns across large trading networks. By surfacing these red flags early, banks can work with customs, tax, and law enforcement agencies to intervene before money laundering schemes become entrenched.

The Road Ahead: Turning Data Linkage into Actionable Intelligence

Despite significant progress, the full promise of data-linkage in TBML detection is only beginning to be realized. Many institutions are still at the early stages of integrating external data sources, often facing technical, legal, and organizational hurdles. Issues around data privacy, cross-border information sharing, and interoperability of government and private-sector systems can slow progress. However, global trends are clear: regulatory pressure is mounting, data sources are proliferating, and the cost of siloed compliance is becoming unsustainable.

Looking forward, compliance leaders should focus on several strategic priorities. First, invest in technology that enables flexible, secure, and scalable data-linkage—this means not only onboarding new external data sources, but also automating linkage and pattern recognition across vast datasets. Second, develop robust governance around data quality, access control, and privacy, ensuring all linked data is accurate, up to date, and handled within legal frameworks like GDPR and US privacy law. Third, foster industry-wide collaboration by participating in sectoral utilities, information sharing partnerships, and public-private data initiatives that bring together shipping, finance, and law enforcement stakeholders.

The future will see AI and machine learning supercharge data-linkage, with algorithms detecting not just known typologies but emerging patterns of trade manipulation, UBO obfuscation, and container reuse. Scenario-based testing and simulation will allow compliance officers to model new TBML risks and stress-test their controls before criminals exploit the next loophole.

At its core, the value of data-linkage is the transformation of scattered facts into actionable intelligence. It turns every invoice, container number, and UBO registry entry into a puzzle piece—one that, when connected, exposes the larger picture of financial crime. For compliance teams fighting TBML, this shift is more than an upgrade. It’s a revolution.

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Related Links

• FATF Guidance: Trade-Based Money Laundering
• EU Beneficial Ownership Registers
• World Customs Organization: Data Model
• US Corporate Transparency Act

Other FinCrime Central Articles About Data-Linkage, and TBML

• Revealing Hidden Connections: How Universal Data Linkage Can Revolutionize Financial Crime Detection
• Game-Changer for AML: Satellite Data Reveals Undetected TBML Schemes
• Next-Level AML Monitoring Can Be Achieved with Connected Supply Chains

Some of FinCrime Central’s articles may have been enriched or edited with the help of AI tools. It may contain unintentional errors.

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