FOR DATA ANALYTICS
For data analytics & insights teams in property insurance
Standard catastrophe models reflect flood vulnerability of the built infrastructure, but can't evaluate modeled loss outcomes for flood prevention measures. Link hazard, vulnerability, and flood prevention measures to generate portfolio analytics that support better pricing, portfolio risk analysis, and reinsurance decisions by incorporating the impact of prevention measures on modeled expected loss.
Trusted by industry leaders
Why Mitigrate
You can model the portfolio. You can't explain how prevention measures can improve the risk profile, or what would change it.
Standard catastrophe models do not have the property-level inputs needed to model prevention ROI.
01
Vulnerability data exists — but rarely reaches the analytics layer
Without structured vulnerability data, flood risk gets approximated through proxies — producing outputs that are harder to defend than they need to be.
02
Prevention ROI stays unquantifiable
Without structured vulnerability data, the cost of prevention measures and their expected loss reduction can't be mapped at property level — making ROI impossible to calculate.
03
Exposure without property-level drivers
Portfolio models show where exposure concentrates — but stop short of connecting property-level vulnerability to actual loss outcomes.
04
Prevention measures don't show up in PML estimates
PML estimates reflect hazard exposure, but vulnerability models do not accurately account for the loss reduction potential of prevention measures already in place.
In practice
Property-level data layer for prevention analytics
The property-level data layer that connects portfolio models to prevention intelligence.
Mitigrate provides the property-level dataset that connects flood hazard to vulnerability, mitigation, and expected loss reduction — giving analytics teams a foundation to build prevention intelligence across underwriting, claims, exposure management, and capital allocation simultaneously.
01
Attributes linked to damage functions
Link flood depth, building category, soil type, and land cover to damage functions and vulnerability curves
02
Vulnerability connected to loss outcomes
Connect property-level vulnerability drivers to loss outcomes — before and after mitigation
03
Prevention ROI modelled at portfolio level
Model prevention ROI at portfolio level and communicate it to underwriting, claims, and actuarial teams.
04
Structured data replacing proxies
Reduce reliance on proxies and assumptions with structured, alternative property-level data
Trusted by insurers acting on flood risk at scale.
“
Mitigrate's innovative approach to providing property-level flood risk assessments represents a crucial advancement in loss prevention. The ability of Prevent to quantify effectiveness and prioritise interventions at scale makes them a valuable partner in bringing prevention to life — to make reinsurance and insurance more sustainable and accessible, where carriers who invest in prevention get rewarded for it.
Joseph Norris
Head of UX & inland catastrophe analytics | Aon
Model-agnostic
Compatible with leading hazard model outputs including JBA, Fathom, and Copernicus
Instant reports
Property-level prevention reports generated automatically and distributed to claims handlers or loss adjusters within seconds of an event
1:20 – 1:100
Prevention ROI modelled at 1:20 and 1:100 — before and after mitigation
One layer, every team
A single prevention analytics layer serving underwriting, claims, exposure management, and actuarial.
FAQ
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