Agentic AI in trade finance

Trade finance does not usually fail because teams do not work hard. It fails because the operating model is built around a fragile assumption: that scarce experts can continue adjudicating complex documents, at scale, under tight rules, with zero tolerance for mistakes. That assumption is breaking.

It is therefore no surprise that GenAI has entered trade finance with significant momentum. What is more surprising is how many initiatives are being set up to disappoint. The pattern is predictable. A compelling demo, usually focused on extraction and summarization, is followed by a pilot in a sandbox. Then comes silence, prompted by the first real question from governance: Can you defend this decision—consistently—under audit, sanctions scrutiny, dispute escalation, and legal challenge?

Most pilots cannot. The reason is simple: trade finance is not primarily a knowledge problem. It is an evidence and adjudication problem. When solutions are not designed for this reality, “agentic AI” risks becoming an expensive prototype, impressive in a workshop, but unusable in production.

Letter of credit (LC) document scrutiny is one of the most document-heavy and rule-driven processes in banking. A single LC case can involve up to ~39 different document types and 200+ data elements that must be cross-checked across documents and LC terms—often under strict timelines and with limited tolerance for error.

This creates a tangible operational bottleneck:

• Turnaround time: Manual LC scrutiny can take 2+ hours per transaction.
• Rework and errors: Manual scrutiny can drive frequent mistakes and re-processing; some baselines reference ~10% human error.
• Scalability: Manual LC processing may handle ~50 transactions/day, while growth scenarios demand ~100/day without doubling headcount.

When agentic designs are built with the right controls (not just “automation”), banks are typically targeting measurable outcomes such as:

• Reduce processing time from ~2 hours to ~0.5 hours per transaction (~75% turnaround time improvement)
• Reduce rework/error from ~10% to ~5% (~50% improvement)
• Increase throughput from ~50/day to ~100/day (~2x capacity)
• Improve rules consistency by enforcing structured checks aligned to UCP (Uniform Customs and Practice for Documentary Credits)/ ISBP (International Standard Banking Practice)/ ICC (International Chamber of Commerce) sanctions frameworks, and any local policy overlays (including Sharia where relevant)

The point is not the exact number. The point is that trade finance offers one of the clearest “adjudication at scale” use cases where benefits are large—but only if the design survives governance.

Trade operations are often described as a clean pipeline: ingest, extract, validate, decide, and communicate. It is a familiar diagram. However, the real friction sits in the judgement layer. In documentary trade, letters of credit provide the clearest illustration, though similar dynamics apply across collections and guarantees. The difficult work lies in interpretation rather than reading. Clauses appear straightforward until edge cases force judgement. Real-world documentation is inconsistent, and minor variations frequently trigger technical discrepancies that are commercially routine. Tolerances introduce further complexity, as quantity and value interact with invoice phrasing, units, and narrative descriptions.

Exceptions also tend to cluster around predictable friction points, including partial shipments, transshipment wording, “clean on board” statements, and tight date conditions. Bilingual document packs add another layer of complexity. Translation alone is not sufficient; structure, labels, and formatting can subtly shift meaning and directly affect how checks must be applied across Arabic and English documentation.

This is adjudication. When GenAI programmes focus primarily on better OCR (Optical Character Recognition) or more polished summaries, they may improve convenience, but they rarely change the economics of the process. Those economics are driven by exceptions, disputes, and defensibility.

A useful distinction emerges when examining why initiatives succeed or stall. When the design goal is simply to reduce effort, the result is often a solution that fails to survive governance. When the design goal is to produce a decision artefact that can withstand governance scrutiny, the programme has a path to production.

In regulated banking operations, every decision must be traceable, reproducible, grounded in approved standards and policy, bounded by clear constraints, and fully auditable through a complete evidence bundle rather than a collection of logs. This is where many initiatives falter. Intelligence is built first, while controls are treated as an afterthought. In trade finance, controls are not an overlay. They are the product.

There are three failure modes we witness repeatedly.

A trade-grade agentic design looks less like a single, large model and more like a disciplined system of specialized components, each with a clearly defined responsibility. These include establishing authoritative terms baselines, managing intake and classification, handling translation and localization without losing structure, building a canonical record of presented documents, validating compliance with standards and policy, producing structured discrepancies tied to evidence, and generating communications supported by references and artefacts. A human review interface supports maker–checker workflows with full evidence visibility rather than summaries alone.

Overseeing all of this is an orchestrator that sequences steps, enforces routing rules, captures provenance, and compiles a complete audit bundle. That audit bundle is non-negotiable. If a solution cannot produce a regulator-ready, dispute-ready package for every transaction, it is merely a demonstration rather than an operating model.

Trade finance is inherently global, and the GCC holds a distinct advantage. The region has both the appetite for step-change digitization and the ability to align stakeholders more rapidly than many mature markets. At the same time, its operating reality intensifies the challenge. Multilingual documentation is common, counterparties are diverse, sanctions and financial crime controls are high-stakes, and data residency considerations are increasingly important.

The longer-term shift is not simply toward paperless trade. It is toward proof-based trade, where counterparties exchange verifiable outcomes supported by tamper-evident audit trails. Outcomes move across borders while sensitive raw data does not. This reduces friction without compromising jurisdictional control.

Agentic AI can deliver meaningful improvements, including shorter cycle times, fewer rework loops, greater consistency in discrepancy handling, higher throughput without linear staffing growth, and stronger integrity and fraud detection. However, speed is easy to market and easy to misrepresent.

What truly matters is whether a bank can deliver consistent adjudication with a defensible trail, at scale, without introducing new risks. This is the point at which the economics of trade operations genuinely change.

Agentic AI can deliver meaningful improvements, including shorter cycle times, fewer rework loops, greater consistency in discrepancy handling, higher throughput without linear staffing growth, and stronger integrity and fraud detection. However, speed is easy to market and easy to misrepresent.

What truly matters is whether a bank can deliver consistent adjudication with a defensible trail, at scale, without introducing new risks. This is the point at which the economics of trade operations genuinely change.

A practical approach is straightforward, but it requires discipline. Organizations should begin with a high-friction adjudication use case, define the exception taxonomy and what constitutes defensible evidence, build trust rails early through controlled policy grounding and immutable audit bundles, industrialize orchestration within the live operating environment, and measure quality rather than speed alone.

In practice, that quality lens should be quantified. Beyond headline cycle-time metrics, leading indicators typically include discrepancy precision, rework reduction, audit readiness, and throughput capacity under controlled governance.

When this sequence is followed, organizations avoid a common trap: deploying intelligence before control.

If an agentic AI solution cannot produce a regulator-ready, dispute-ready audit bundle for every decision, it is not agentic trade finance. It is an expensive prototype. The organizations that succeed will not be those with the most impressive demonstrations. They will be the ones that redesign the operating model around evidence, control, and accountability from the outset.