What Happens When AI Misses a Trade Discrepancy?

"What's your accuracy rate?"

It's the first question we get in every conversation with commodity trading operations teams. And it should be. When you're dealing with cargoes worth millions of dollars, a missed discrepancy on a bill of lading can cascade into demurrage fees, LC rejections, and strained counterparty relationships.

So let's talk honestly about AI accuracy—and more importantly, what happens when AI gets it wrong.

The 99% Problem

AI document processing has reached impressive accuracy rates. Modern systems routinely hit 95-99% on well-scanned documents with standard formats. But in commodity trading, that 1-5% matters enormously.

Consider the math: a mid-sized trading operation might process 500 documents per month. At 99% accuracy, that's 5 potential errors. At 98%, it's 10. Each error that slips through could mean a missed quantity discrepancy, an incorrect port code, or a pricing mismatch that nobody catches until the vessel is at discharge.

The honest answer is that no system—human or AI—achieves 100% accuracy. The real question is what happens when mistakes occur.

Why AI Makes Different Mistakes Than Humans

AI and human operators fail in different ways. Humans get fatigued during long sessions. They're more likely to miss errors on the 47th document than the first. They develop blind spots for familiar counterparties and common formats.

AI has its own failure patterns. It struggles with poor scan quality, unusual document layouts, and handwritten annotations. It can misread ambiguous characters—confusing "0" with "O," or "1" with "l". It may confidently extract a wrong value because the document format didn't match its training data.

Understanding these different failure modes is key to building robust operations. The goal isn't to eliminate mistakes entirely—it's to ensure mistakes get caught before they cause damage.

Building Safety Nets

The most reliable trade operations combine AI processing with intelligent safeguards. Confidence scoring means uncertain extractions get flagged for review rather than passed through silently. Cross-document validation catches when quantities don't match across the bill of lading and commercial invoice. Tolerance rules distinguish genuine discrepancies from noise like rounding differences. And exception-based review focuses human attention where it actually adds value.

The Comparison That Matters

When evaluating AI accuracy, the right comparison isn't "AI versus perfect." It's "AI plus human review versus human-only review."

Manual reconciliation isn't error-free. Studies consistently show that human error rates increase with volume and fatigue. An operations team processing 50 documents per day will miss things that an AI-assisted workflow would catch.

The question becomes: does AI plus human review catch more errors than humans alone? In our experience, the answer is clearly yes—not because AI is infallible, but because AI and humans catch different types of errors.

How Mercora Addresses This

Mercora processes trade documents with confidence scoring on every extracted field. When our AI is uncertain—whether due to scan quality, unusual formatting, or ambiguous values—it tells you. The dashboard surfaces documents that need attention, ranked by confidence and discrepancy severity.

We don't ask you to trust blindly. Every extraction is reviewable. Corrections flow back into the system to improve future accuracy. Human judgment stays in the loop where it belongs.

The goal isn't to replace your operations team's expertise. It's to multiply their effectiveness by handling the routine work and flagging the exceptions.

Get in touch to see how Mercora handles your documents—including the edge cases.