2026-06-10 / model evaluation / document review / AI metrics

Model Evaluation Metrics for Supplier Document Review

Accuracy is not enough. Verification models need field-level, case-level, and escalation-level evaluation.

Why it matters

Supplier document review is not a single classification task. A useful AI system extracts fields, compares entities, detects mismatches, summarizes evidence, and recommends escalation. Each layer needs its own evaluation because a model can perform well in one area and fail in another.

Evidence to collect

Measure OCR field accuracy, name matching precision and recall, document classification accuracy, hallucination rate in summaries, escalation trigger performance, and analyst correction frequency. Keep a labeled set of messy real-world examples, not only clean test documents.

How to review it

Evaluate by decision impact. A minor punctuation error may not matter. A wrong beneficiary match or missed expiry date can change payment risk. Metrics should weight critical fields more heavily than low-risk text.

Where buyers get misled

Teams get misled when they report one high accuracy number. That number may hide failures on rare but important cases, especially entity mismatch, stale certificates, or altered documents.

Practical next step

Create a model scorecard tied to verification outcomes. Review it whenever data sources, document types, or model versions change.