Is Precision the Most Important Metric in AI Testing
- Rohit Rajendran
- Aug 5
- 3 min read
Precision often gets the spotlight in AI testing. It measures the share of correct positive predictions. A precision of 95 percent means that when the model says “yes” it is right 95 times out of 100. Sounds impressive. Yet precision alone never tells the whole story, especially in trade finance.
Why one metric is never enough
Every AI system makes two kinds of mistakes. It can predict “yes” when the answer is “no” or predict “no” when the answer is “yes.” Precision covers the first case. Recall covers the second case. The F1 score combines them. Good testing weighs all three in light of real costs.
Trade finance example
Document type classification
A large bank ingests thousands of PDFs every day. Letters of credit, bills of lading, inspection certificates, and invoices land in the same drop folder. The model tags each file with a document type so downstream checks can start.
Precision focus
If the model mislabels an invoice as a letter of credit, the workflow stops. Operations staff must open the file, identify the error, update back-end systems, and explain the delay to the client. Each mislabel can cost an hour of work and hundreds of dollars in idle capital. Here a high precision reduces rework.
Recall trade-off
If the model rejects a valid invoice because it is unsure, the file still reaches a human. The team fixes the label in minutes and moves on. The cost is smaller than a wrong positive. A balanced approach values precision more than recall for this task.
Sanctions screening
Banks must screen counterparties against sanctions lists and politically exposed person lists. Missing one prohibited name can trigger multimillion-dollar fines.
Recall focus
A false negative exposes the bank to penalties and reputational loss. A false positive means an analyst spends extra time clearing the alert. That cost is tiny compared to a missed hit. So recall outranks precision here. The model can raise more alerts than needed as long as it rarely misses a real match.
Putting numbers to the risk
A quick cost matrix clarifies choices.
Error type | Example task | Average cost per error (approximately) |
Wrong positive | Document type mislabel | $150 rework and one-hour delay |
Wrong negative | Sanctions miss | $5,000,000 fine plus reputation hit |
Wrong extraction value | Duty calculation | $500 adjustment and client complaint |
With numbers in hand you can answer “Which metric matters most?” by picking the one that drives total cost down.
Practical tips for testers
Build a confusion matrix for each use case. Map costs to each cell.
Tune thresholds with business owners in the loop. Set one threshold for screening and another for autoflow.
Track precision and recall per document class, not just overall. High macro averages can hide niche errors that still hurt business.
Add synthetic edge cases. For sanctions models include uncommon name formats and machine-readable passports.
Report metric shifts in plain language. “We raised recall to 99 percent for sanctions names that include non-Latin characters.”
Final thought
Chasing one metric without context wastes effort. First list the real costs of each error. Then pick the mix of precision, recall, and F1 that cuts those costs. The right metric is the one that protects the business and keeps clients happy.
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