Biji Kurian John, Principal Product Development Manager at Eastnets explores why trade finance is restricted by fragmented data, and how institutions can break the cycle.
For many trade operations teams, managing modern financial crime risk feels like a behemoth task. One minute it’s a sanctions alert, the next a pricing anomaly and then a counterparty concern emerging elsewhere in the transaction chain.
Just as one issue is resolved, the process resets, forcing teams to rebuild their view of a transaction from fragmented data all over again. At times, it can feel more like pushing a boulder uphill, only for it to roll back down again.
And as sanctions regimes expand and global trade becomes more fragmented, financial institutions are under mounting pressure to detect illicit activity hidden within increasingly complex trade flows. Yet transactions span multiple jurisdictions, involve layers of counterparties and rely on data that’s often inconsistent, unstructured and disconnected across systems.
Many institutions respond to issues as they surface rather than seeing the full picture. This only raises a more fundamental question: why isn’t the current operational model equipped to keep pace with the demands of modern trade?
The growing pressure on trade finance
The heart of this challenge is not simply the scale of trade finance, but the way it’s structured and processed. Behind every transaction sits a vast web of documents, messages and systems, spanning everything from invoices and bills of lading to corporate registries and customs data. Much of this information lacks standardisation, exists in different formats and is distributed across disconnected systems.
For trade operations teams, constructing a complete and reliable view of a single transaction often means piecing together fragmented data from multiple sources, multiple jurisdictions, each with varying levels of accuracy and accessibility. This fragmentation goes beyond operational inconvenience as it directly weakens the ability to detect financial crime. In fact, less than 1% of global illicit flows are successfully detected. So, when data is inconsistent or incomplete, key risk indicators can be missed, misinterpreted or deprioritised. As a result, progress made in one part of the process is rarely retained across the next, forcing teams to repeatedly reconstruct the same view from scratch.
This challenge only intensifies when considering the increasing expectations placed on financial institutions. Regulatory frameworks such as AMLD6, AMLA and FATF guidance are raising the bar for detecting complex risks like mis-invoicing and trade-based money laundering. Yet institutions are being asked to detect more complex threats without the data needed to validate them, widening the gap between regulatory expectations and operational reality.
The cross-border nature of trade further intensifies this problem too. Identity data, corporate ownership records and goods information vary significantly between jurisdictions, often lacking interoperability or consistency. Combine that with the regulatory push toward digitisation and transparency through initiatives like eIDAS 2.0 and MLETR, and you’ll find this is uneven across jurisdictions, limiting its practical impact. Therefore, establishing a clear view of who is involved in a transaction – and what’s actually being traded – remains a persistent challenge, leaving large portions of trade activity in a grey area where transactions cannot be easily classified.
For operations teams, this translates into a continuous cycle of manual intervention, duplication of effort and prolonged investigation timelines. Even with increased investment in AI and advanced monitoring tools, the effectiveness of these systems is inherently restricted by the quality of the data they rely on. What this means is that more sophisticated technology can end up just amplifying the same inefficiencies, scaling false positives and uncertainty rather than resolving them.
As regulatory expectations increase and trade flows grow evermore complex, institutions are being asked to deliver greater levels of insight and control without the foundational data needed to support it. The issue is not simply one of process or technology, but of visibility.
Rethinking how trade data is used
What’s becoming increasingly clear is that incremental improvements to existing processes is not enough. The real solution lies in how trade operations are structured to manage and interpret the data that underpins it.
A growing number of financial institutions are, therefore, shifting towards a more data-centric approach to trade operations and moving beyond document-led processes, to one where trade data is aggregated, structured and analysed in context. Rather than relying on fragmented inputs across multiple systems, this approach focuses on building a more complete and consistent view of each transaction, so that insight can be carried forward rather than recreated at each stage.
This shift is also changing in tandem to how financial crime detection is approached. Instead of treating compliance as a downstream activity – typically applied after transactions have been processed – there’s a move towards embedding risk assessment directly into the trade lifecycle. Screening, monitoring and anomaly detection must increasingly be integrated into operational workflows, allowing institutions to identify potential issues earlier and with greater context, rather than reacting after the fact.
At the same time, institutions are beginning to place greater emphasis on the quality and usability of the data itself. This includes structuring previously unstandardised information, enriching datasets with external sources and improving consistency at the point of origin. The objective is not simply to process more data, but to make it more meaningful, creating the conditions in which advanced analytics and AI can be applied effectively.
Taken together, these developments point towards a more integrated operational model for trade finance, one where data, compliance and workflows are no longer treated as separate functions, but as interconnected pieces of a single system. By reducing fragmentation and supporting greater visibility across the trade lifecycle, institutions are better positioned to manage risk while maintaining the speed and efficiency required to support global trade.
Breaking the cycle
As trade finance continues to evolve, the ability to manage financial crime risk will depend less on adding new controls, and more on rethinking how trade operations are designed from the ground up.
That’s why institutions need operating models that can adapt and have that consistent visibility across transactions, counterparties and jurisdictions, rather than responding to issues in isolation. For too long, progress in trade operations has been difficult to sustain; rebuilt with each new alert, exception or investigation, rather than carried forward.
The opportunity is there to redesign trade operations so that progress is not lost with every transaction, but instead where insights build over time, rather than resetting with each new alert, exception or investigation. Much like the Greek myth of Sisyphus, the real challenge for trade finance teams is not how hard they push the boulder, but instead understanding why it keeps rolling back down, and how to stop it.
