Every few years, trade finance gets caught up in a capital-rules debate. The current one is significant. Under the proposed U.S. implementation of the Basel Endgame reforms, banks could be required to set aside 10% of certain supply chain finance exposures as upfront capital because of a proposed broadening of the definition of a bank’s “commitment” to extend credit, purchase assets or issue credit substitutes.
The proposal has triggered concern among industry groups. The Bankers Association for Finance and Trade (BAFT) has suggested that the generally short-term and self-liquidating nature of supply chain finance does not justify the additional capital burden. Other industry associations have also raised concerns about the treatment of trade finance and the use of credit insurance as a credit-risk mitigant.
These are important regulatory questions. Banks and industry bodies should continue to make the case for proportionate treatment.
But there is another issue underneath the debate that deserves more attention:
Why better data strengthens the regulatory case
Capital frameworks are designed around the characteristics and risk of exposures. In the current debate around supply chain finance, industry arguments for more proportionate treatment rely in part on the short-term and self-liquidating characteristics of these transactions.
That makes transaction-level visibility increasingly important.
A bank that can consistently identify the structure, tenor, counterparties, documents and other characteristics of its exposures is better positioned to support its risk-management and regulatory arguments than a bank relying on fragmented documentation and manual processes.
That is where the data problem becomes important.
In many trade finance environments, the information needed to understand a transaction still sits across scanned documents, PDFs, emails, legacy systems and manually entered fields. A bank may have all the underlying documentation, but that does not mean the information within those documents is immediately available as structured, machine-readable data.
This is not necessarily a question of whether the underlying trade finance business is riskier than the industry believes. It is a question of how quickly and consistently a bank can access the evidence needed to understand, process and control that business.
Paper-based trade is a global problem, not just a bank problem
The challenge extends well beyond individual banks.
The Organisation for Economic Co-operation and Development (OECD)’s 2025 report on the digitalisation of trade documents and processes notes that, despite rapid digitalisation across the economy, international trade in goods remains heavily reliant on paper-ased processes.
The potential economic impact is significant. The Organisation for Economic Co-operation and Development (OECD) estimates that a 10% improvement in bilateral performance in automating border procedures, combined with streamlined documentation and greater co-operation among border agencies, could boost global goods exports by up to 18%.
That is more than an operational efficiency story. It illustrates how much friction remains embedded in the infrastructure of global trade.
The same issue appears inside banks.
Letters of credit, bills of lading, packing lists, certificates of origin, invoices and other trade documents contain the information required to process transactions and perform controls. The challenge is that the information is not always available in the structured form that operations, compliance, risk and reporting teams need.
Digital trade and document digitization are not the same thing
It is useful to separate two conversations that are often treated as one.
Electronic bills of lading, legal frameworks based on the Model Law on Electronic Transferable Records (MLETR), and DCSA standards are addressing an important ecosystem-level challenge: enabling trade documents to exist electronically, move between participants and gain legal recognition where applicable.
The United Nations Commission on International Trade Law (UNCITRAL)‘s Model Law on Electronic Transferable Records is specifically designed to enable the legal use of electronic transferable records, including documents such as bills of lading.
The Digital Container Shipping Association (DCSA)‘s Bill of Lading standards similarly focus on standardised data exchange between shippers, carriers and consignees, with the objective of reducing manual intervention and improving interoperability.
Those are essential developments. But they involve multiple parties, systems, standards and legal frameworks. No individual bank can solve the broader interoperability and legal-recognition challenge on its own.
The internal data problem is different.
A bank does not need every participant in the global trade ecosystem to become fully digital before it can improve the way it processes the documents it already receives.
A letter of credit presentation does not have to arrive as a native electronic instrument for a bank to extract structured, actionable information from it.
That means document digitization is not simply a future-state ambition. It can be an operational improvement available today.
What still happens on many trade finance desks
Across many trade finance environments, document-heavy workflows still involve a familiar sequence.
Documents may be scanned or uploaded. They are classified by type. Relevant information may be entered or validated manually in bank systems. A maker examines presented documents against the letter of credit terms, applicable UCP 600 rules, and International Standard Banking Practice (ISBP 821) guidance. Names and entities are screened against sanctions requirements. Where relevant, trade-based money laundering risk is assessed using information such as vessels, jurisdictions, counterparties and other transaction indicators. A checker then provides the required control or approval before the transaction progresses.
The precise workflow varies by bank, product and level of automation. But the underlying challenge is consistent: critical information remains trapped in documents and requires significant human effort to make it usable.
None of these processes requires the underlying document to become an electronic transferable record before they can be improved.
Document classification, data extraction, discrepancy identification, sanctions screening and TBML risk assessment can increasingly operate on structured information derived from the documents banks already process.
That is a document intelligence problem and one banks can address within their existing operating environment.
Digitization is not the same as transformation
Digital transformation in trade finance does not have to begin with replacing a bank’s core platform.
In many cases, a more practical starting point is to make the documents flowing through existing systems more usable.
That means automatically classifying documents, extracting the fields that were previously rekeyed or manually validated, identifying inconsistencies across documents, and feeding structured information into the maker-checker processes already used by trade operations teams.
The benefits can compound across the workflow.
Banks can increase transaction volumes without increasing operational headcount at the same rate. Controls that previously depended on repetitive manual checks can become more consistent and easier to evidence. Risk processes — including sanctions screening, TBML review and discrepancy management — can work from current, structured information rather than requiring every decision to begin with a person reading a document from scratch.
The objective is not to remove human expertise.
It is to move human attention toward the decisions that actually require judgement.
Where AI fits into trade finance
This is where the AI conversation becomes more useful.
AI in trade finance does not have to mean replacing the bank’s existing platform or attempting to automate every decision.
A more practical model is to use AI as an intelligence layer around the workflows the bank already operates:
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Document intelligence to classify and interpret complex trade documents
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Data extraction to convert unstructured information into structured fields
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Workflow orchestration to move transactions and exceptions to the right next step
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Compliance controls to support sanctions, discrepancy and TBML processes
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Human-in-the-loop review for exceptions, ambiguity and decisions requiring judgement
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Structured data to create a more consistent foundation for risk, reporting and operational analytics
At Cleareye.ai, this is the opportunity we see in AI-powered trade finance: not technology for its own sake, but intelligence applied to the documents, decisions and workflows that already sit at the heart of trade operations.
The goal is to make existing processes faster, more consistent and more scalable — while keeping the controls and human expertise that trade finance requires.
The stronger position when the rule finally lands
Banks do not have to wait. They can start with the data problem they already own.
The U.S. capital-rules process will continue to evolve. Whatever the final treatment of supply chain finance looks like, banks will still need to understand their exposures, manage risk and demonstrate that their processes are controlled and defensible.
That makes the investment in structured trade data useful regardless of the final regulatory outcome.
Banks that can understand their trade finance book transaction by transaction, supported by consistent and structured information, will have a stronger operational foundation than those still dependent on fragmented documentation and manual processes.
The benefit is not limited to capital discussions.
Better document data can support faster processing, more consistent controls, improved exception management, stronger compliance processes and greater scalability.
The global trade system is moving toward more digital and paperless processes, but that transition will take time. It depends on standards, interoperability, legal recognition and coordination across a large number of participants. OECD research makes clear that these ecosystem-level changes are important — and that the transition remains incomplete.
Banks do not have to wait for that transition to finish.
They can start by solving the data problem they already own.
Key Takeaways
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The proposed U.S. Basel Endgame rules could require banks to set aside 10% upfront capital against certain supply chain finance exposures, increasing the potential cost of providing these products.
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Better transaction-level data can strengthen a bank’s ability to understand, manage and evidence its trade finance exposures, even though digitization itself does not guarantee preferential regulatory treatment.
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The OECD estimates that a 10% improvement in relevant trade-facilitation measures for automating border processes, combined with streamlined documentation and stronger agency cooperation, could increase global goods exports by up to 18%.
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Global trade remains heavily reliant on paper-based processes, despite significant progress in digitalisation.
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Electronic bills of lading, MLETR and DCSA standards address the broader ecosystem challenge of digital trade, while document intelligence addresses a more immediate internal bank problem.
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Banks do not necessarily need to replace their core systems to begin digitizing trade documents. Structured data can be introduced into existing workflows to improve processing, controls and scalability.
- The most useful role for AI in trade finance is not replacing expertise, butaugmenting it — turning complex documents into structured information and allowing people to focus on judgement, exceptions and higher-value decisions.
Sources
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Jacob Atkins, “Mooted US capital rules could harm supply chain finance, industry warns,” Global Trade Review, June 24, 2026.
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OECD, “The Digitalisation of Trade Documents and Processes,” OECD Trade Policy Paper No. 297, September 2025.
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UNCITRAL, “Model Law on Electronic Transferable Records (2017).”
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. DCSA, “Bill of Lading Standard.”
