AI Won't Replace Your Trade Ops Team. Here's What It Will Do.

Every bank technology vendor is selling the same dream right now: feed your letters of credit into our AI system and watch it spit out discrepancy reports in seconds. No more human review. No more training junior examiners. No more $50-per-document processing costs.

It is a compelling pitch. It is also wrong — or at least, it is wrong in the way it is being sold.

What AI can actually do today

Let me be specific about what current AI systems — including large language models and specialized document processing tools — can reliably do with trade finance documents:

Extract structured data from unstructured documents. A well-trained system can read a bill of lading, packing list, certificate of origin, or commercial invoice and extract key fields (shipper, consignee, port of loading, description of goods, quantities, dates) with 92-96% accuracy. For clean, well-formatted documents, accuracy approaches 99%.

Cross-reference fields across documents. Once fields are extracted, comparing them against each other and against the LC terms is straightforward logic. Does the shipper on the B/L match the beneficiary on the LC? Does the port of loading match? Is the shipment date within the LC validity period? These checks are deterministic once you have clean data.

Flag obvious discrepancies. If the LC says "PORT OF LOADING: SHANGHAI" and the B/L says "PORT OF LOADING: NINGBO," any competent system will flag that. Same for quantity mismatches, date violations, and missing documents.

What AI cannot reliably do

Interpret intent. When a certificate of origin states "COUNTRY OF ORIGIN: P.R.C." and the LC requires "COUNTRY OF ORIGIN: CHINA," is that a discrepancy? A human examiner knows it is not. An AI system needs to be explicitly taught every equivalence — and the universe of equivalences in trade documents is essentially infinite.

Apply judgment to borderline cases. The "clean on board" scenario from our last Discrepancy column is a perfect example. The rules are clear, but applying them requires understanding industry practice, not just parsing text. AI systems that flag "per shipper's count and weight" as a discrepancy are technically being cautious. They are also being wrong.

Navigate the politics. A significant portion of trade finance operations is not about documents at all. It is about relationships, risk appetite, and commercial pressure. The issuing bank that rejects a $50M presentation on a technicality is often making a credit decision dressed up as a compliance decision. No AI system can detect that — and no AI system should be making that call.

The useful middle ground

The real value of AI in trade operations is not replacement. It is triage.

A well-implemented system can process 100 document sets and sort them into three buckets:

1. Clean (estimated 30-40% of presentations): All fields match, no discrepancies, standard document formats. These can be approved with minimal human review — a senior examiner glancing at the AI's output for 60 seconds instead of doing a full 20-minute review.

2. Flagged (estimated 40-50%): Potential discrepancies identified. The AI shows the examiner exactly where the issues are, with the relevant LC clause highlighted alongside the document text. The examiner still makes the call, but their review time drops from 20 minutes to 5.

3. Complex (estimated 15-20%): Non-standard documents, unusual clauses, documents in multiple languages, or presentations where the AI's confidence is low. These go to senior examiners for full manual review, just as they do today.

This triage model does not eliminate trade ops jobs. But it does change what those jobs look like. Junior examiners spend less time on mechanical checking and more time on learning judgment. Senior examiners handle higher volumes because the easy cases are pre-screened. The bank processes more LCs with the same headcount — or the same LCs with fewer errors.

The bottom line

If a vendor tells you their AI will "automate" your LC processing, ask them what their false negative rate is. Ask them how they handle non-standard document formats. Ask them what happens when the AI is wrong and a discrepant presentation is approved.

If they have good answers, you are probably looking at a useful tool. If they change the subject, you are looking at a demo that works on clean data and breaks on real-world documents.

The future of trade finance operations is not AI replacing humans. It is AI making humans faster, more accurate, and able to focus on the work that actually requires expertise. That is less exciting than "full automation." It is also what will actually happen.