Insurance Profitability

Price the flood risk others miss — and protect your property portfolio's profitability

After applying our models across four continents and dozens of insurers, clear claims patterns have emerged — patterns that can inform pricing at very detailed levels.

Book demo Learn more

What is different about our approach?

Property-Level Accuracy

1-meter resolution captures the risk that coarser models miss — down to the building, the kerb, the drain.

Always Current

Continuous reprocessing ensures risk scores reflect the latest land-use, construction, and terrain conditions.

Claims-Calibrated

Scores built and validated on real, verified flood insurance claims — not synthetic scenarios or proxies.

How granular flood risk data drives insurance profitability

Flood risk can vary dramatically between neighboring properties. Yet most pricing models miss it entirely. Applied across four continents and dozens of insurers, consistent claims patterns have emerged — giving carriers the granular insight needed to price accurately and protect profitability:

Premiums show little discrimination across flood risk categories — even where traditional flood models had been applied. High-risk and low-risk properties tend to be priced the same.
Flood claims tell a different story. Loss ratios are multiples higher in our elevated-risk categories, showing a strong correlation with 7Analytics’ Pluvial Flood Index.

The implication is clear: the risk is always there. It just isn’t visible. Armed with Pluvial Flood Index data, the insurer has a strong basis for re-pricing and portfolio rebalancing.

Try it yourself

Book a demo

We make it easy to validate the value and accuracy of our pluvial flood index — with just a few steps, you can get high-resolution flood risk insights tailored to your exact locations.

Proof of concept — from the field

Download full case study

US & EuropeCase study

Pricing showed zero discrimination — until we overlaid the PFI

Two independent validation streams — one across almost a million properties in the US, one spanning 6 million buildings across 12 European insurers — returned the same finding: granular pluvial data stratifies loss ratios that conventional models leave flat.

The blind spot: Premium distribution tends to be flat across all PFI bands despite incorporating traditional flood model inputs. High-risk properties are priced identically to low-risk neighbours.

The signal: When claims history is overlaid, loss ratios vary by multiples across PFI bands — rising sharply and accelerating non-linearly at the upper end.

Adverse selection exposed: Market penetration is inversely correlated with risk score — a classic adverse selection signature.

~1M

US properties validated

European property portfolio

5-10×

loss ratio/claims frequency,
high vs. low PFI

The cost of mispriced flood risk

Mispricing flood risk has a direct P&L impact.

Underpricing high-risk buildings attracts the worst risks. Overpricing low-risk buildings drives away your best customers.

You lose the profitable business; you keep the unprofitable.

Benefits