What Is a Confidence Score?
A confidence score refers to a numerical or categorical rating applied to an automated result. In MCA and small business lending, confidence scores are applied during intake and scrubbing steps when documents, fields, or transactions are parsed.
A high score signals that the automation is certain about the output, while a lower score indicates that human review may be necessary.
This score typically appears in workflows where precision is critical, such as extracting balances, detecting risk flags, or classifying documents. Operators use it to manage exception queues effectively and avoid bottlenecks.
How Does a Confidence Score Work?
Confidence scoring combines data checks with validation logic.
- Parsing validation: Automation measures the clarity of extracted fields, such as whether a number is readable or ambiguous.
- Cross-checking: Results are compared across multiple inputs (e.g., totals vs. transaction sums) to confirm consistency.
- Scoring logic: The system assigns a confidence percentage or category to the result.
- Routing: High-confidence outputs flow directly to underwriting, while low-confidence outputs are escalated to review.
In Heron, confidence scores are built into the scrubbing engine.
- Automated parsing: Data is extracted from documents or emails during intake.
- Confidence calculation: Heron assigns confidence levels to each extracted field or output.
- Structured outputs: Scores are written into CRM fields alongside parsed data.
- Next action: Submissions with high-confidence outputs move forward automatically, while edge cases are routed into exception queues for human review.
This balance makes sure automation accelerates most cases while still maintaining safeguards.
Why Is a Confidence Score Important?
For brokers and funders, confidence scores are important because they reduce risk while allowing automation to scale. Without them, automation would either pass through errors unchecked or slow down every case by requiring manual validation.
Heron makes confidence scoring valuable by embedding it into every scrubbing workflow. Teams gain speed on routine submissions while still protecting accuracy by routing uncertain cases to review.
Common Use Cases
Confidence scores are used to improve reliability in submission workflows.
- Routing high-confidence outputs directly to underwriting queues.
- Flagging low-confidence cases for exception review.
- Writing scores into CRM fields for transparency on each data point.
- Tracking exception rates to measure automation performance.
- Reducing underwriter workload by focusing only on edge cases.
FAQs About Confidence Score
How does Heron calculate confidence scores?
Heron calculates confidence scores by measuring clarity of extracted fields, checking for consistency across data points, and applying internal validation rules.
Why are confidence scores important for MCA brokers and funders?
They allow automation to run at scale without sacrificing accuracy. High-confidence items move quickly, while low-confidence cases are safely reviewed.
What outputs should teams expect from confidence scoring?
Teams receive structured CRM fields that show confidence levels for key data points, enabling them to see which fields are reliable and which require review.