Insights

Last updated: June 16, 2026

The Insights tab provides a high level look into your SOV. Insights are available after your SOV has been fully cleaned and enhanced on Archipelago – typically 24 hours after being uploaded.

The Insights page has two sections:

Account Summary:

  • Overview: Includes Total Total Insurable Value, Number of Properties, and Data Quality Score.

  • Highlights: An AI Generated Summary that covers your account's geographic distribution, predominant occupancy, catastrophe exposure, construction & protection and data quality.

  • Peril Exposure Summary: Uses Archipelago's Hazard Mapping to show how much of the SOV is exposed to Seismic, Hurricane, Severe Convective Storm (SCS), Flood, and Wildfire. Click on Details and Severity Charts to see a breakdown of total insured value across modeled hazard classes and severity bands from your latest property revisions. Click into each hazard class bar to drill down further

  • TIV Components and Subvalues: Breaks down the SOV Total Insurable Value into Building Replacement Cost (BRC), Contents and Business Interruption (BI). Click into each to break down the components further.

  • Map View: Your properties are grouped and placed as dots on a map and labeled by TIV. You can click on each dot to zoom to that section.

  • COPE and Secondary Modifiers: Charts the SOV's Construction, Occupancy, Year Built and Roof Age by TIV. Hover over each bar to see the full TIV number and percentage of TIV. Click on each bar to see which properties are represented in each and their top level details. Each chart can be downloaded via the hamburger icon (top-right corner)

    image.png

Data Quality Summary: Provides a full breakdown of Archipelago's data quality score and how complete your SOV's data is.

Archipelago's unique data quality score provides an overall rating for a portfolio's data completeness and quality based on components most impactful to insurers and insurance models. Each property is scored individually, then TIV-weighted for the account-level score. We save the score before and after our data enrichment process.

​