Preparing a letter of credit: what to automate and what to keep with people

Software can already prefill the application from the contract, check facility headroom, screen parties and track expiry dates. AI can draft documentary clauses and flag mismatches. People still agree terms with the counterparty, judge risk and sign. Clean contract and party data come first.

Where the work actually starts

On paper, preparation begins when a signed contract reaches treasury or trade finance. In practice it often begins earlier, in an email thread where sales or procurement has already promised the other side a particular bank, a tight shipment window or a document nobody can easily produce. By the time the request arrives, some terms are fixed and some are only implied.

That gap matters for automation. A tool can only work from what is written down. If the real terms live in a sales manager's inbox, the tool will produce a tidy application built on the wrong facts.

Steps software handles well today

The mechanical parts are ready to hand over. Bank portals and trade platforms accept structured applications, and a system can fill most fields directly from the contract record: applicant and beneficiary details, amount and currency, latest shipment date, expiry, place of presentation, shipping terms.

Checking available headroom on the bank facility is another good candidate. So is screening every named party, vessel and port against sanctions lists before anything goes to the bank. These checks are rule based and repetitive, and a machine does them consistently every time.

Once issued, the credit becomes a commitment that finance has to see. Posting it to memorandum accounts in the ledger, and feeding the expected payment into the cash forecast as a large disbursement, can both run automatically from the issuance confirmation. The same record can drive reminders as expiry and shipment dates approach, which is where many amendments start.

Where AI helps, with a reviewer

The drafting of documentary requirements is where errors creep in. Goods descriptions that differ slightly from the invoice, inspection certificates from bodies the beneficiary cannot reach, or a document list that contradicts the agreed shipping terms all lead to discrepancies later.

A language model can read the contract and propose clause wording from an approved library. It can also compare a draft credit against the contract and point out conflicts, such as a required document that only makes sense under a different shipping term. This is useful. It still needs someone who knows the trade to accept or reject each suggestion, because the model cannot tell which conflicts the commercial team has deliberately agreed to.

Steps that stay with a person

Negotiating terms with the counterparty is human work. So is deciding whether a confirmed credit is worth the cost for a given country or bank, or whether a standby would serve better than a documentary credit.

Sanctions hits need judgement. Most are false matches on common names, but clearing one is a decision someone must own and record. The same applies to unusual clauses requested by the beneficiary, transferable or revolving structures, and any request to waive a standard protection.

Final approval and signature belong with authorised signatories under the bank mandate. Internal control reviews will look for evidence that the approver saw the full draft, not just a summary line in a workflow tool, so the approval record has to show what was actually reviewed.

What the data has to look like first

Before any of this is automated, a few things need to be true.

Counterparty master data must carry full legal names and registered addresses exactly as they appear on trade documents. Abbreviations and trading names cause both screening noise and discrepancies.

Contracts need their key terms held as fields, not buried in attached text. Shipping terms, tolerances on quantity and value, partial shipment rules and the agreed document list should each be captured where a system can read them.

Facility limits and utilisation have to reflect what the bank holds, refreshed often enough that a headroom check means something. A clause library also has to exist, with wording that legal and the banks have accepted. Without one, AI drafting simply reproduces whatever variations the last few credits happened to use.

Finally, the link between the credit record and the ledger needs a stable reference, so that commitments, fees and eventual payments can be traced back to a single instrument.

Questions to ask the people who run it

• When a request arrives, what do you usually have to chase before you can start the application?
• Which terms do sales or procurement agree informally that never reach the contract?
• Which bank wording do you change by hand every time, and why?
• What caused the last discrepancy that delayed payment, and could it have been seen at drafting?
• How do you check facility headroom today, and how current is that figure?
• Who clears a sanctions match, and where is that decision recorded?
• When a credit is amended, how does finance find out?
• Is there a spreadsheet or personal tracker that the system does not replace?
• What do auditors usually ask for when they sample these instruments?

APQC's Process Classification Framework® (PCF) is an open standard developed by APQC, a nonprofit that promotes benchmarking and best practices worldwide. To download the full PCF or to view definitions and measures, please visit www.apqc.org/pcf.