What happens when AI is uncertain?

Ambiguity is normal in bordereaux processing. A vague heading, changed workbook or incomplete value may support more than one plausible interpretation.

Humans recognise this and pause. An AI-assisted workflow needs an equivalent ability to withhold acceptance and ask for review.

That ability cannot depend solely on a confidence score. Systems expose uncertainty in different ways, and a model may still be confidently wrong. Safe handling combines several signals with validation, evidence and named decision routes.

Uncertainty has several observable forms

Some systems provide a score for a field or mapping. A low score, or two similarly ranked alternatives, can indicate that the evidence does not favour one answer strongly.

Uncertainty can also appear without a useful score. A source layout may be unfamiliar. A column heading and its values may suggest different meanings. Repeated runs or components may produce inconsistent results. A proposed mapping may fail a target-schema rule or conflict with a related field.

These signals are not interchangeable. A known rule failure proves that an approved condition was not met. Uncertainty means the available evidence does not support safe acceptance. A business referral may arise even when the data is clear because an authorised person must make the decision.

The workflow should label the reason accurately so that the right person receives the case and later reporting is meaningful.

Traditional exception controls define safe handling

Delegated authority teams already hold records, raise queries, reject incomplete submissions and escalate material issues. AI should connect to those controls rather than bypass them.

Define conditions under which automatic processing must stop. These may include an unknown source format, missing business identifier, competing material mappings, failed reconciliation or a value that requires contract interpretation.

The held case should retain the original submission, affected field or record, proposed alternatives, validation results and any rule or component version involved. Reviewers need evidence, not a bare alert saying the AI is unsure.

Some conditions can be resolved with deterministic logic. Others require a bordereaux analyst, technical accountant, claims specialist, underwriter or the submitting coverholder. Routing should reflect the meaning and authority involved.

AI can expose alternatives without resolving them

AI can make uncertainty easier to review. It can show possible mappings, identify neighbouring fields, compare past approved examples and summarise the source evidence supporting each interpretation.

This is useful preparation, but a plausible explanation does not settle the business meaning. A column labelled Net Amount may look like net premium based on its values. If the coverholder uses it for premium after brokerage, only the relevant definition, contract context or source clarification establishes the correct mapping.

The model should not be asked repeatedly until it produces the answer someone prefers. Nor should a reviewer approve the top-ranked suggestion merely because it is presented confidently.

Where evidence remains insufficient, the correct outcome is continued hold, escalation or a source query. Unresolved uncertainty should remain visible.

Every uncertain case needs a disposition

An effective review ends with a recorded outcome: accept with evidence, correct, request information, reject, or escalate to an authorised owner. Record who decided, why, when and which data or definition supported the decision.

The workflow should then release only the affected data that meets its acceptance conditions. Material dependencies may require the whole bordereau to remain on hold, while an approved partial-processing policy may allow unaffected records to proceed.

Monitor uncertainty by source, format, field, reason and age. A rising queue can indicate changed data, poor threshold design, missing reference knowledge or inadequate reviewer capacity. Repeated cases may justify a new approved mapping or rule.

Feedback becomes production knowledge through change control. Reviewer decisions should be validated, versioned and tested before reuse; they should not silently retrain or reconfigure the live system.

Example

A hypothetical premium bordereau contains a column labelled Net Amount. Its values could represent net premium or gross premium after brokerage.

The AI ranks both mappings closely and the target checks cannot distinguish them. The workflow holds the column, displays related premium and commission fields and routes the case to technical accounting.

The reviewer uses the contract section and a coverholder definition to confirm the meaning. The decision and evidence are recorded, and the approved mapping enters controlled change for future submissions. The workflow never loads the more confident guess simply to keep processing moving.

FAQs

• Does every AI output have a confidence score?

  No. Systems differ, and an available score may not be calibrated for the task or production population. Use confidence alongside novelty, consistency, validation and outcome evidence.

• Should all low-confidence output be rejected?

  No. Hold and route it according to materiality and available evidence. An authorised reviewer can then accept, correct, query, reject or escalate it under approved controls.

• What if the reviewer also cannot decide?

  Keep the output on hold and escalate to the authorised business owner or submitting party. Lack of evidence should not be converted into an assumed fact.