Separate client-declared cover from inferred insurance gaps

An agency may infer that a client lacks a line because similar businesses often hold it. That inference is useful for deciding what to ask, but it is not a fact about the client. Evidence labels help an AI-assisted report or account review avoid overstating what the agency actually knows.

Define the data before the metric

One row represents: one client-line observation with source, observation date and confidence status.

Useful fields: Client ID, line, observation type, source reference, observed date, reviewer, declared placement, current verification status and unresolved questions.

Use a small evidence vocabulary: documented, client-declared, inferred and unknown. Preserve contradictory observations for review rather than letting the latest import overwrite them without explanation. Require an up-to-date basis before presenting an external-placement amount or a confirmed coverage absence. Keep peer comparisons as prompts for discovery.

Worked example

The following records and amounts are invented to show the method. They are not customer results, industry benchmarks or a forecast of Covirage performance.

Only the first record has documentary placement evidence in this example. The second records a client statement; the third identifies uncertainty. A report saying 'the client has no cyber cover' would exceed the evidence. A better output asks the account team to confirm the current arrangement.

Use the result in a review

1. Have account staff review conflicts and stale observations before using them in a client discussion.
2. Show source and observation date beside external-placement amounts in analytical views.
3. Use inferred gaps to generate questions rather than definitive coverage assertions or forecast revenue.

Checks before publishing

• Ensure inference labels survive export and summarization instead of disappearing from the final narrative.
• Flag evidence older than the agency's approved review cadence.
• Keep policy-line tags distinct from actual terms, limits and exclusions.

Where this analysis can mislead

Even a policy schedule may be incomplete or out of date, and broad product labels do not prove adequacy. This evidence hierarchy organizes a review; it does not deliver insurance advice.

Explore this question with your own data

Bring a small, authorized sample to Covirage for insurance agencies and brokers. Use the sample to discuss the fields and views your business needs. A dashboard or AI analyst can help explore this question when the required data and definitions are available; missing records still need to be resolved.

Upload sample data to check its structure. Keep unnecessary personal, claims and policyholder details out of an initial sample. The sample check does not establish that every analysis in this guide is available automatically.

Related guides

• Create a cyber coverage inventory for agency account reviews
• Connect commercial auto review records to fleet exposure changes
• Commercial insurance broker KPIs

Reference context

These references provide terminology or governance background. The worked example and proposed review method above are original illustrations, not prescribed industry standards.

• Insurance data context
• AI evaluation guidance