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.
01
The gap between catastrophe model output and actionable underwriting intelligence is a data problem, not just a model problem
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Prevention ROI can't be demonstrated without property-level vulnerability and mitigation data
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Better input data means better models, and more credible outputs across every downstream team
Real pain & daily struggles
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.
05
Same logic rebuilt for every team
The same prevention logic gets rebuilt separately for every team that needs it.
Why this problem exists
Why portfolio models have always stopped short of property-level prevention intelligence
Standard catastrophe models reflect flood vulnerability of the built infrastructure, but can't evaluate what flood prevention investment would do to the loss curve. Without a layer that connects flood depth, building characteristics, soil type, and mitigation measures to damage functions and vulnerability curves, analytics teams are forced to work with incomplete inputs. The result: portfolio-level insight without the granularity to drive prevention ROI or support property-level decisions.
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
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Vulnerability connected to loss outcomes
Connect property-level vulnerability drivers to loss outcomes — before and after mitigation
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Prevention ROI modelled at portfolio level
Model prevention ROI at portfolio level and communicate it to underwriting, claims, and actuarial teams.
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Structured data replacing proxies
Reduce reliance on proxies and assumptions with structured, alternative property-level data
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One analytics layer serving every team
Serve underwriting, claims, exposure management, and reinsurance decisions from a single prevention analytics layer
What success looks like
What you should be able to say at the year-end review.
Portfolio models produce numbers. Connecting those numbers to property-level reality is what makes them credible.
A prevention analytics layer that serves the whole business — from pricing to capital allocation to reinsurance decisions
My inputs are richer and more defensible — fewer proxies, more property-level signal
I can model prevention ROI and communicate it in terms every downstream team understands
Portfolio models connect to property-level reality — not just aggregate exposure
I spend less time defending methodology and more time delivering insight
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.
Proof
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.
Trusted by
Other roles
Underwriters aren’t alone in the carrier. Mitigrate fits the rest of the team.
01
Risk engineers
Direct survey capacity to the properties with the highest loss reduction potential
Read their page →
02
Claims professionals
Turn the post-event window into a prevention opportunity
Read their page →
03
Data analytics
Connect portfolio models to property-level prevention intelligence
Read their page →
Explore Mitigrate for analytics