An AI confidence score is a numeric value, typically between 0 and 1, or expressed as a percentage, that represents how certain an AI agent is about a specific output: an extracted field, a matching decision, a classification, an anomaly flag. In finance applications, it determines whether the output is trusted automatically or escalated for human review.
Confidence scores are critical to making AI finance tools usable in production. A model that returns "supplier X charged €1,247.50" with a 99% confidence score can be processed automatically. The same output with a 62% confidence score must surface as an exception for human review, because the cost of acting on a wrong number outweighs the benefit of automation.
The confidence threshold is configurable. Conservative finance environments, regulated industries, high-value transactions, set the bar at 95% or higher. More tolerant environments accept 80%. The choice is a business decision, not a technical one.
What separates a credible AI finance tool from a generalist one is the transparency of the score: not just the value, but the reasoning behind it. Phacet exposes the confidence score on every agent output, alongside the source document, the rule applied, and the matching logic, visible in the Detail view. The DAF can see why the agent is 87% confident on this invoice match, and decide whether 87% is enough.
This is the foundation of explainable decision control: not a black-box score, but a sourced, explainable measure of certainty that fits into a continuous finance control framework. The score is the trigger; human judgment remains the final authority.
