CASE STUDY · Top-3 U.S. broker
Same buildings. Better data. Lower premium pricing.
−38%
500-year worst-case modeled loss$70.2M → $43.5M
−31%
Average annual loss$558,911 → $386,880
“We re-ran the exact same catastrophe model twice: once with the thin spreadsheet, once with the details filled in. Nothing about the buildings changed. Only the data did, and the modeled loss fell off a cliff.”
Challenge
A thin SOV was being priced as worst-case.
A top-3 U.S. broker took over a client whose schedule of values was missing key COPE information: the construction details that show how well a building withstands wind and water.
Where those fields were blank, the RMS catastrophe model defaulted to conservative assumptions. The portfolio was being priced for risk it did not actually carry, simply because the record could not prove otherwise.
Results
Better data, lower modeled loss.
The team enriched the SOV with the missing construction and protection detail, then re-ran the identical model. The buildings were unchanged; the inputs were complete.
Modeled loss drop sharply across both worst-case loss and the average loss, driving price reductions as carriers better learnt the risk
−38%
Modeled loss500-year worst-case modeled loss
−31%
Modeled lossaverage annual loss
~5 min
Effortof owner input to enrich the record
50+
Datasources connected to every property
Conclusion
Open helped turn a thin spreadsheet into a record the model could trust, cutting insurance pricing without touching a single building.
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