AI Agents

Context

Built for banking within the Trade Finance Invoice Factoring stack and used by Trade Finance and Operations teams, this agent focuses on a specific risk point: confirming that goods were actually delivered before financed funds go out. It is triggered when an invoice is presented for factoring and the bank has delivery notes or proof-of-delivery documents that must match the invoice and recipient.

What it does

The agent reads the invoice details—buyer, supplier, delivery address, dates, amounts, references—and compares them with delivery confirmations and signed proof-of-delivery documents. It checks that the right party signed, that signatures and names line up with the expected recipient, and that dates, locations, and document references are consistent with the financed invoice. Where something does not match, it flags the discrepancy and writes a clear “matched / mismatch / missing” status back into the trade finance workflow so operators know if there is a delivery issue before disbursement.

Core AI functions

The core capability is cross-document matching. The agent extracts key fields and signatures from delivery notes and confirmations, aligns them with invoice and buyer data, and tests for consistency on identifiers, names, dates, and amounts. It turns what is usually a manual “does this POD really belong to this invoice and consignee?” check into a repeatable, system-driven control.

Problem solved

Without this agent, undelivered or partially delivered goods can still be financed because delivery evidence is checked only superficially or under time pressure. Operators may rely on the presence of “some document” rather than a proper match, and discrepancies between invoice and delivery confirmations are picked up late, if at all. That exposes the bank to financing goods that never arrived or went to the wrong recipient. The agent closes that gap by systematically validating delivery confirmations against the financed invoice.

Business impact

The main impact is fraud and loss reduction. By ensuring that only invoices with properly matched delivery confirmations proceed to disbursement, the bank reduces the risk of financing fictitious or undelivered trades, strengthens its control framework for invoice factoring, and improves the quality of its collateral base. For Tier 1–2 banks running higher volumes, this also cuts rework and post-fact investigations when a financed transaction later turns out to lack proper proof of delivery.

Integration and adjacent use cases

Integration complexity is low: the agent needs access to invoice-level trade finance cases and their associated delivery confirmations or POD documents, and it writes its match status and findings back into the same system.

Common combinations in this stack:

• Invoice Eligibility Validator to confirm invoice authenticity and overall eligibility before delivery checks;

• Line-Item Subsidy Filter to apply program eligibility at invoice line-item level once delivery is confirmed;

• Supporting Documents Completeness (Trade) to ensure that required purchase orders and delivery notes are present and properly attached before matching;

• Supplier Contract & Identity Validator to check that the underlying supplier contracts and counterparties meet program and risk criteria; and

• Trade Finance Exception Handler to aggregate red flags from all these checks and route unclear or high-risk cases to specialists with a consolidated view of discrepancies and recommended next steps.