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How does GEO calculate Exposure Values

Understand how the calculations work within Insights, specifically Estimated Exposure and Damage Adjusted Exposure

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Written by Product Team
Updated over a month ago


Required Columns

Locations:

  • Reference Columns (LocationID & ContractID)

  • Building, Contents and BI Values

  • Building, Contents and BI Limits

  • Building, Contents and BI Deductibles

  • Location Limit, Deductible and Participation

Policy:

  • Reference Column (ContractID)

  • Limit, Attachment, Deductible and Share

If this data is not immediately obvious from the data you provide (e.g. non standard format or CSV) we will look to map these values based on available data. If not available, Policy Insights will not be available for that event.


How the calculation works:

1. Spatial Intersection

When a new event occurs, MIS identifies which of your portfolio locations fall within the event’s affected footprint. Each location that intersects the exposure layer is “exposed.”

2. Applying Terms and Damage Factors to Constituent Values

Each exposed location includes detailed insured values and financial terms across Building, Contents, and Business Interruption (BI).

Policy Insights applies MIS Damage Factors and policy logic in the following order:

For the Estimated Exposure Value we treat the Damage Factor as 100% except in scenarios


Step 1: Start with Constituent Insured Values

Each location has;

  • Building Value

  • Contents Value

  • BI Value

These represent the Total Insured Values (TIV) at the location.


Step 2: Apply Damage Factors

Damage Factors - derived from MIS Intelligence or user input - represent the expected percentage loss for each exposure level.

They are applied individually to the constituent values:

Damage-Adjusted Value = Value × Damage Factor (%)

This yields preliminary estimates for Building, Contents, and BI losses.


Step 3: Apply Location-Level Terms

After adjusting for damage, the following terms are applied at the location level:

  • Building Limit, Contents Limit, BI Limit – caps each category’s recoverable value.

  • Building Deductible, Contents Deductible, BI Deductible – subtracts the applicable excess.

  • Location Limit and Deductible – combined caps or excesses across the whole site.

  • Location Participation – applies any co-insurance or share percentage.

If constituent-level limit or deductible values for Building, Contents, or BI are not available, Policy Insights automatically uses the Location Limit and Deductible fields to apply adjustments at the site level instead.


Step 4: Roll-Up to Policy Level

Once each location’s adjusted exposure is calculated, results are aggregated by ContractID to the policy level.

Policy-level financial terms are then applied:

  • Policy Limit – overall cap on recoverable loss under the policy.

  • Attachment / Excess Point – threshold before recovery begins.

  • Policy Deductible – additional policy-wide deductible amount.

  • Policy Share – share of participation or co-insurance (if applicable).


What You'll See in GEO

You’ll see Estimated Exposure Values represented in:

  • Location Insights Table – individual Building, Contents, BI values and applied factors with (min/max) Damage Adjusted Estimates and an Estimated Exposure value by location.

  • Policy Insights Table – aggregated (min/max) exposure values with applied policy terms, including location counts.

  • Map View – visualised footprints of affected policies and locations.

Example of Policy Insights with Calculated Columns

Example of additional columns visible in Location Insights (may need to double click)


Calculation Assumptions

  1. Every location in Location Insights belongs to every layer for that specific contract/policy.

  2. Policy Terms apply uniformly across locations. (We currently don't apply sub-limits)

  3. Layer exposures in Policy Insights can be summed.

Note: These assumptions are likely to overstate the exposure

To join Location Insights and Policy Insights use the associated Contract/Policy ID column that exists in both datasets.

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