ITFA report examines the growing challenge of dual-use goods detection in trade finance - Trade Treasury Payments

As military spending rises in the wake of heightened geopolitical tensions, the classification and detection of military dual-use (MDU) goods is becomming a growing challenge for trade finance institutions. A renewed regulatory focus on export controls is is prompting many banks and trade finance providers to identify and mitigate risks that, until recently, sat at the periphery of compliance workflows.

Compared to conventional sanctions screening, dual-use detection is more complex,  requiring institutions to assess what is being traded, how it is described, where it is going, and who is involved. This challenge is made more acute by the continued reliance on legacy compliance tools (things like manual reviews, static lists, and keyword filters) that are ill-suited to capture the nuances of modern trade flows.

The International Trade and Forfaiting Association (ITFA) has published a report, The Conquest of Military Dual-Use Goods Detection in Trade Finance, looking at this very issue. 

Trade finance’s new front line

One of the report’s central arguments is that the compliance challenge is not just is structural in addition to being technical. Unlike other areas of trade compliance, where multilateral coordination provides a degree of consistency, MDU regulation is highly fragmented. 

Take semiconductor and supercomputing as an example. In the US, UK, and EU, changes to the control mechanisms for these widely used components are generally introduced unilaterally, with a limited cross-border alignment that creates divergences in scope, terminology, and enforcement expectations.

Operationally, this regulatory inconsistency can create visibility gaps. Many MDU goods are classified at a product or model level, yet trade documentation often refers to high-level descriptions. Even where Harmonised System (HS) codes or Export Control Classification Numbers (ECCNs) are present, they may not be sufficient to determine risk.

The ITFA report acknowledges that no single screening method is likely to capture all relevant risks, which makes a layered approach all the more important. This could include combining structured data, internal and external sources, artificial intelligence (AI), and contextual risk factors such as destination routing, transshipment, or the use of shell companies to narrow the aperture of risk in a defensible and scalable way.

Building smarter detection capabilities

Articicial Intelligence and Machine Learning (AI/ML) models, when trained on well-labelled historical data, can be built to flag potential MDU indicators that static systems are liable to miss.

Yet, as with anything, deploying a sophisticated technology is not enough. Institutions need small, cross-functional teams (usually comprising compliance officers, data scientists, technologists, and business leads) who can test models and interpret results. Equally important is the ability to explain those results using metrics such as confusion matrices, AUC scores, or Shapley values to demonstrate why a transaction was flagged and how a model reached its conclusion.

Then there is the question of what kind of model to use. When it comes to MDU compliance should an institution build the technology itself, use a pre-built vendor solution, or find a suitable hybrid of the two. All of these options have different implications when it comes to things like cost, control, scalability, and data governance. Off-the-shelf tools may be faster and easier to deploy, but in-house systems can be more readily aligned with institutional risk appetite and IT constraints. 

Whatever the model, strong governance, clear service level agreements (SLAs), and ongoing model validation will need to be established. 

Operationalising resilience

With defence becoming more of a political priority, detecting dual-use goods is becoming a more mainstream concern. As export control regimes expand and enforcement expectations rise, trade finance institutions will need to respond with systems that are more adaptive, data-driven, and integrated with broader risk functions.

ITFAs report does not claim to offer definitive answers but it frames the problem in practical terms and outlines the conditions under which different solutions might be viable. It recognises that list-based screening remains embedded in many workflows, but argues that institutions now have access to tools (like structured datasets, real-time APIs, trained ML models) that can move detection beyond reactive flagging and towards proactive risk segmentation.

Ultimately, effective MDU detection is a question of alignment between technology, internal teams, external vendors, evolving regulations, and institutional risk tolerance. For those looking to better understand the terrain, the full report is worth reading.