Trade finance is, at its core, a risk mitigation tool built on interpretation, application, accountability and documentary assessment. And, as is known, AI becomes useful where it improves consistency, traceability, accessibility and operational understanding.
In our view, the ICC Banking Commission should take steps to position AI primarily as a practical tool for consistency, operational efficiency, process effectiveness, and knowledge management, supporting better interpretation, analysis, and application of ICC rules. This would certainly be more effective than attempting to replace human judgement or promote technology for its own sake.
Bearing in mind that the Banking Commission possesses decades of Opinions, DOCDEX decisions, Technical Advisory Briefings, National Committee discussions, plenary papers and interpretative history scattered across publications, PDFs and institutional memory, the most immediate and valuable use is likely to be knowledge accessibility. Much of that knowledge is difficult for banks, corporates and younger practitioners to navigate quickly, whereas an ICC-controlled AI knowledge layer could allow practitioners to ask operational questions in natural language and receive grounded answers linked directly to source material, including UCP articles, ISBP paragraphs, Opinions and related guidance. The important point is that the AI should not invent interpretation. It should function as an intelligent retrieval and guidance layer with citations and explainability.
There is also a strong role in education. The Banking Commission has historically relied heavily on seminars, workshops, PowerPoints and expert-led training. AI now makes scenario-driven learning possible at scale. An examiner could work through realistic document presentations, refusal scenarios or even eUCP hybrid presentations interactively, with the system explaining not only whether something is compliant, but why, where judgement is involved, and which ICC texts support the reasoning. That
Another important area is consistency analysis. One of the Banking Commission's recurring challenges is that different banks, regions and examiners interpret similar situations differently. AI could be used internally to analyse patterns across Opinions, National Committee submissions and recurring discrepancy disputes leading, over time, to a situation whereby they could identify where confusion repeatedly emerges, for example around freight wording, combined document requirements, insurance clauses, electronic presentation mechanics or "on its face" standards. That would help prioritise Technical Advisory Briefings or educational material without immediately pushing toward formal rule revision.
The digital trade agenda is another obvious fit. As electronic presentations, structured data, APIs and digital negotiable instruments expand under frameworks such as ICC Digital Standards Initiative and MLETR-aligned systems, AI becomes increasingly relevant in validating structured trade data, detecting anomalies, reconciling data sets and supporting hybrid paper/electronic examination processes. The Banking Commission should not attempt to become a software provider, but it can establish principles around explainability, auditability, data lineage and ICC-aligned decision transparency. Those themes are far more important than whether a system calls itself "AI".
ICC-aligned decision transparency is perhaps the most important of all. If AI is used in trade finance operations, its outputs should remain traceable to recognised ICC rules, standards and practice such as UCP 600, ISBP, URDG, eUCP or future DSI standards. In practice, that could mean AI systems identifying not merely that a discrepancy exists, but specifically linking it to the relevant UCP article, ISBP paragraph or institutional policy rationale. The Banking Commission does not need to build software to influence this environment, but it can shape industry expectations by publishing guidance, model governance principles, examination standards, recommended controls and operational frameworks that technology providers, banks and platforms can align themselves to. That creates consistency and trust across the market without the ICC itself becoming a technology vendor.
There is also a defensive reason for ICC involvement. AI tools are already being used inside banks and by fintechs such as Traydstream and Complidata. If ICC does not engage, the market risks fragmented and opaque implementations where proprietary logic gradually becomes de facto interpretation. The Commission therefore has an important role in defining boundaries. AI should support examination, not replace bank responsibility under UCP 600, because the bank must remain responsible for the determination. Systems should produce traceable reasoning, preserve audit evidence and distinguish clearly between objective rule checking and subjective interpretive assessment.
The Banking Commission could also use AI operationally within its own structure. National Committee feedback, consultation responses and working group discussions are increasingly large and difficult to synthesise. AI-assisted thematic analysis could help identify common concerns globally, consolidate recurring issues and highlight areas requiring clarification. Used properly, that could make the Commission more responsive without making it more bureaucratic.
This is probably one of the strongest and most realistic near-term use cases for AI within the Banking Commission, because working groups already operate through iterative drafting, consultation and consensus-building. At present, many working groups lose enormous amounts of time manually consolidating overlapping comments, resolving duplicated observations, comparing slightly different drafting proposals and trying to identify where genuine disagreement actually exists. Often, thirty pages of comments may boil down to three real issues hidden beneath different wording styles and regional perspectives.
A much more structured AI-assisted process could work with the working group first preparing a controlled consultation paper or question set. Instead of broad open-ended emails, the questions are structured by issue category. For example:
National Committees then submit responses either through a portal or standardised template. The important thing is that responses become machine-readable and consistently tagged from the outset, allowing AI to then perform several extremely valuable functions simultaneously. Firstly, it consolidates duplication, e.g., if fifteen NCs indicate that the rules are unclear regarding status of electronic records where the credit is silent, the system recognises this as a single thematic concern even where wording differs significantly.
Secondly, it identifies divergence, because AI can separate broad consensus areas, partial agreement with drafting differences, and fundamentally conflicting positions. This becomes enormously useful as working groups often waste time re-visiting issues where consensus already exists whilst underestimating areas where real disagreement remains unresolved.
Thirdly, the system can generate comparative summaries, e.g., 22 NCs support clarification via guidance rather than rule revision, 8 NCs favour amendments to eUCP definitions, or 14 NCs expressed concern regarding operational handling rather than legal framework.
We are now at a stage where the working group can see the scene more clearly rather than reading hundreds of disconnected comments individually. This ensures that the next stage becomes even more powerful, on the basis that AI can prepare a neutral synthesis draft for circulation back to the working group. It can also automatically identify unresolved points requiring targeted follow-up questions.
That is exactly where AI becomes valuable, not in replacing expertise, but in narrowing ambiguity and improving the quality of the next consultation round, with the process becoming iterative.
Done properly, this could fundamentally improve the responsiveness of Banking Commission working groups without changing the authority structure or turning ICC into a technology organisation.