Prepare an insurance book data handover for analytical review

A transferred book can arrive as several spreadsheets whose client counts and income totals refer to different dates. The receiving team needs a consistent analytical perimeter before it can discuss retention or servicing needs. A good handover preserves the source evidence instead of relying on a single headline book value.

Define the data before the metric

One row represents: one source extract and its control, plus stable client and term records within the agreed handover perimeter.

Useful fields: Source owner, extract date, period, client and term IDs, signed commission and fee history, expiry, current ownership, transfer inclusion date, exclusions and exception status.

List every file, its grain and control total. Review identity and line mappings without assuming the receiving agency's codes match the source. Preserve origin tags and valid inclusion dates. Keep privacy authorization and permitted sharing explicit; use minimized samples before receiving a broader dataset.

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.

The $420,000 income control can reconcile even while $15,000 lacks a client mapping. That amount should remain in an exception bucket rather than disappearing from the handover. The 350 IDs should not be called 350 confirmed relationships until the duplicate and grouping review is complete.

Use the result in a review

1. Resolve material unmatched income and missing near-term expiries before relying on client-level forecasts.
2. Have source and receiving operations staff agree the perimeter and mappings, keeping signed-off evidence.
3. Track the imported book separately in growth reports so handover relationships are not mislabeled as newly won clients.

Checks before publishing

• Compare every exported total with its source report for the same date and perimeter.
• Keep original IDs and a reviewed crosswalk instead of replacing them without trace.
• Confirm the authority to share data and remove unnecessary personal or claims details from initial review files.

Where this analysis can mislead

This checklist covers analytical data readiness only. It does not establish valuation, title to relationships, contractual rights, regulatory compliance or deal suitability. Appropriate professionals should handle those questions.

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

• Compare agency income per FTE on a consistent basis
• Analyze ageing of agency policy-change service requests
• 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.

• Privacy considerations
• Agency benchmarking context