Document Capture Confidence Threshold Simulator
Every capture tool lets you decide how confident it must be before a document skips review. Set it too low and errors reach your ERP; set it too high and reviewers drown. This simulator makes the trade-off visible as two cost curves and a total, so the threshold becomes a decision rather than a default.
Your numbers
Read this from your tool's confidence report.
Points of auto-accept share lost for each point of threshold raised (and gained when lowered).
Relative fall in error rate for each point the threshold is raised.
Rework, payment correction, vendor contact and so on.
Your results
A simple two-parameter model anchored at a 90 threshold. Replace the anchors and slopes with values read from your own confidence-versus-correctness report.
Monthly cost vs confidence threshold
Sweeps Auto-accept confidence threshold from 70 to 99, holding your other inputs fixed.
Calibrate thresholds on your real documents
We can run a confidence-versus-correctness calibration on an anonymised sample and give you a recommended threshold per field class. Optional follow-up.
Optional. The tool and every export work without this. No spam.
How to use the simulator
Take a calibration run from your tool, if it provides one: the share of documents that would auto-accept at several thresholds and the error rate among them, found by checking a sample. Enter the values at the 90 point, plus how fast the auto-accept share falls and the error rate improves as you raise the threshold. Add reviewer time and cost, and the cost of one error that escapes. The chart then sweeps the threshold from 70 to 99 and plots review cost, error cost and the total.
- Calibrate with real, checked samples, not the tool's own claims
- Estimate escaped error cost from actual rework cases
- Look for the bottom of the total curve, then check how flat it is
The model
Auto-accept share is assumed to fall linearly as the threshold rises, because fewer documents reach the bar. The error rate among accepted documents is assumed to fall geometrically, since the lower confidence tail is what you are removing. Review cost is the review volume times minutes times rate. Error cost is accepted volume times error rate times the cost of a single error. The sum has a minimum where the marginal review cost equals the marginal errors avoided, which is the economic threshold.
Reading the curve
A flat valley means the exact threshold is not critical and you can favor the lower review load. A steep rise on the left tells you errors are expensive. Different fields deserve different thresholds, so run it separately for money fields and for free text. Remember that a regulated or high-value document, such as a certificate used for release, may justify a threshold far above the economic optimum, because one escaped error has an unbounded cost.
- Run separate scenarios for high and low value document types
- Re-run monthly while the model is learning
- Never use the simulator to set thresholds for safety or compliance documents without a risk review
Frequently Asked Questions
What does a confidence score actually mean?
It is the tool's internal estimate that a value is correct, and the scale differs between products. A score of 0.9 in one tool is not equivalent to 0.9 in another, so always calibrate the score against checked outcomes from your own documents.
Should I use one threshold for all fields?
Usually not. Amounts, tax IDs and PO numbers carry higher consequences than descriptions or dates, so they deserve stricter thresholds. Many tools allow per field thresholds, and the simulator can be run once per field class.
How do I get the anchor values?
Export confidence scores for a few hundred processed documents, have a person verify them, and bucket results by score. The share above each score and the error rate within that share give you the anchor and slopes.
Why is my total cost curve nearly flat?
A flat curve means review cost and error cost trade off almost evenly across the range. Choose the lower threshold to reduce review load, but watch for rare high-cost errors that the average cost per error may understate.
If you want thresholds set from a measured calibration on your own documents, and a review queue that feeds corrections back to the model, we can build that on your own infrastructure with human approval on every write-back.
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