By Hassan Zebdeh, Financial Crime Advisor at Eastnets
Trade-based financial crime (TBFC) poses a major threat to global finance, siphoning away trillions of dollars every year and eluding even the most vigilant regulators.
Techniques like double invoicing, non-shipment of paid-for goods, or over-invoicing are commonly used to disguise illegal activities as legitimate business deals. One example involves inflating the price of a shipment, such as laptops, to twice its actual value. When the payment is made, the excess money is laundered into the economy.
The scale of this problem is staggering. Between 2008 and 2017, the global value gap between imports and exports reached $8.7 trillion[1], an amount that surpasses even the most recent U.S. federal budget[2]. Last year alone, over $1.1 trillion in illicit funds flowed through financial systems across the Americas[3].
A sizeable portion of TBFC involves trade-based money laundering (TBML), which accounts for around 30% of all money laundering activities[4]. Yet, detection remains a major challenge, with only a fraction of money laundering reports addressing TBML. Fragmented processes, scattered documentation, and limited visibility within institutions hinder effective intervention.
The industry requires more than just human expertise. It needs an AI-enabled software robot – a TBML Terminator – that never sleeps, never misses a detail, and is capable of processing vast amounts of complex data with unparalleled speed and precision.
Eyes on the trade
At the core of this idea are advanced monitoring technologies – the eyes with an unwavering gaze. These technologies continuously watch over trade finance activities to detect suspicious behaviour in real time.
Vessel tracking is a critical part of this monitoring process, given that most global trade moves by sea[5]. This makes maritime shipping particularly vulnerable to exploitation by those seeking to mask illicit activities. By monitoring ship ownership and movements in trade finance deals, this digital sentinel could provide real-time alerts if a vessel enters a high-risk or sanctioned area.
Another vital monitoring function would be document digitisation. This process is crucial given the mountains of paperwork involved in international trade. Everything from bills of lading to commercial invoices must be meticulously captured to pass this information on to the other technologies that might form a TBML Terminator.
Tireless hands
Once the eyes have captured and converted the necessary information, the hands take over. Automation could step in to handle the vast amounts of data generated, organising and managing it with precision.
In the context of trade finance compliance, automation plays a crucial role in grouping and linking the various components of a trade finance deal. This includes consolidating data from transaction messages, such as letters of credit, payment instructions, and contract amendments, alongside documents like commercial invoices, bills of lading, and vessel tracking updates. By seamlessly integrating these elements, automation ensures that all relevant data is accurately linked and ready for further analysis.
A key part of this work involves sanctions screening, where the hands quickly check all entities against constantly updating sanctions lists. Automation could prevent prohibited individuals or organisations slipping through the cracks, safeguarding financial institutions against regulatory breaches and fines.
By taking over these repetitive, time-consuming tasks, automation boosts efficiency and significantly reduces the risk of human error.
The thinking brain
While automation handles the heavy lifting, AI would act as the intelligence driving the TBML Terminator’s advanced analysis and decision-making processes. AI’s capabilities extend far beyond traditional rule-based methods, enabling financial institutions to detect and respond to TBML techniques with a level of precision and speed that manual methods can’t.
For example, AI applications like natural language processing (NLP) are used to structure unstructured documents, turning scanned PDFs into structured XML formats. This allows the system to accurately extract and analyse crucial information from trade documents, such as commercial invoices and bills of lading.
AI models also play a crucial role in identifying TBML techniques, such as overpricing, double invoicing, and the misuse of dual-use goods. By cross-verifying product descriptions with market values, a task that generative AI excels at, financial institutions can quickly identify discrepancies that may indicate illicit activities. This level of scrutiny is essential in uncovering hidden financial crimes that might otherwise go unnoticed.
A unified force
The true power of this TBML Terminator would lie in the seamless integration of these technologies into a unified platform that offers a comprehensive view of each trade finance deal. This integration could enable continuous monitoring and screening of all relevant data, ensuring that any updates or new risks are promptly detected and managed.
This seamless communication between different components – automation, AI, and advanced monitoring – would provide financial institutions with a 360-degree view of trade finance deals. This holistic perspective is crucial for quick, informed decision-making, allowing institutions to act swiftly in response to any potential threats.
Turning the tables
As the complexity of global trade continues to grow, the combination of automation, AI, and advanced monitoring, stands as the key ally against TBFC. Together, these technologies offer financial institutions a proactive and efficient way to combat this pervasive threat.
By staying ahead of these illicit activities, financial institutions will be far better equipped to protect themselves from risk, contribute to a more stable global economy, and safeguard the livelihoods of people around the world.
ENDS
[1] https://gfintegrity.org/report/trade-related-illicit-financial-flows-in-135-developing-countries-2008-2017/
[2] https://fiscaldata.treasury.gov/americas-finance-guide/federal-spending/
[3] https://elements.visualcapitalist.com/wp-content/uploads/2024/04/1711973384569.pdf
[4] https://www.fatf-gafi.org/content/dam/fatf-gafi/reports/Trade-Based-Money-Laundering-Trends-and-Developments.pdf
[5] https://unctad.org/topic/transport-and-trade-logistics/review-of-maritime-transport
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