Understanding AI Confidence Scores in Transaction Matching

What confidence scores mean and how to use them to improve your workflow.

By David Park · · 6 min read

Understanding AI Confidence Scores in Transaction Matching

AI doesn't just categorize—it tells you how sure it is. Here's how to use that information.

What is a Confidence Score?

A confidence score (0-100%) indicates how certain the AI is about its prediction:

  • 90-100%: Very confident, likely correct
  • 70-89%: Fairly confident, worth reviewing
  • 50-69%: Uncertain, needs human review
  • <50%: Low confidence, requires attention

How Scores Are Calculated

The AI considers:

  • Historical pattern matching
  • Description analysis
  • Amount patterns
  • Vendor recognition
  • Seasonal factors

Using Scores Effectively

High Confidence (90%+)

  • Auto-approve these transactions
  • Spot-check occasionally
  • Trust the AI

Medium Confidence (70-89%)

  • Quick review recommended
  • Often correct, sometimes needs adjustment
  • Good training opportunities

Low Confidence (<70%)

  • Always review manually
  • Provide corrections to train AI
  • May indicate unusual transactions

Improving Scores Over Time

  1. Correct mistakes promptly: AI learns from corrections
  2. Be consistent: Use same categories for same vendors
  3. Add vendor rules: Create rules for recurring items
  4. Review patterns: Check what causes low scores

Score Thresholds in Ledger Flow

Customize your automation:

  • Set auto-approve threshold (default: 95%)
  • Configure review queue (default: 70-94%)
  • Alert threshold for anomalies

Real-World Impact

After 3 months of training:

  • 80% of transactions auto-approved
  • 15% quick-reviewed
  • 5% need attention
  • Overall accuracy: 99.5%