Unlocking A Complete Picture of Peril Risk
Sep 2023
An overview of Betterview’s accurate selection of third-party hazard data available through our partner program, Partner Connect
Sep 2023
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Since this post was originally published, Betterview was acquired by Nearmap. References to Betterview, the company, or its products have been maintained for historical accuracy. Betterview is now the primary solution for the insurance industry at Nearmap.
The right peril data makes a tremendous difference for P&C insurers – especially as states like Florida and California introduce new regulations around evolving climatic risks. That’s why Betterview has assembled the most accurate selection of third-party hazard data (available through our partner program, Partner Connect, alongside inspection tools, business data, and other sources of geospatial data) and combined it with our own property-level vulnerability data for a complete view of peril risk. This unique combination empowers insurers to make immediate, critical decisions to protect policyholders and reduce losses.
Peril Data in the Betterview Platform
Sourced from the most innovative and trusted companies in the industry and enhanced with AI-powered vulnerability insights, our peril data is all insurers need to identify exposures, anticipate damage, and protect customers.
- Protect: Leverage multi-factor risk scores to identify every property’s risk of damage in the event of a peril such as wildfire, hurricane, or convective storm. Work together with policyholders to proactively mitigate risk, building trust and avoiding future claims.
- Grow: Expand your business in high-risk areas and avoid major payouts in the future by identifying the properties most exposed to damage, based on predictive risk scores, regional hazard data, and vulnerability factors like defensible space and roof condition.
- Optimize: Centralize all your peril data, including regional and property-specific factors, into a single platform to save time lost switching between multiple screens, driving higher underwriting efficiency.
A Complete Picture of Risk
P&C Insurers have long had access to regional hazard data and CAT models, but this alone does not provide a complete picture of peril risk. The crucial missing factor is vulnerability – the likelihood that a specific structure will experience damage based on property condition. Our machine learning models are built to reveal property vulnerability, identifying factors like missing shingles, roof stains, roof material, and defensible space. These factors are turned into vulnerability scores for specific perils and combined with third-party regional hazard data to reveal overall risk:
- Wildfire Risk: Fire hydrant location, fire station proximity, regional event history including acreage burnt, and the Betterview Wildfire Vulnerability Score
- Hurricane Risk: Maximum wind speed, average barometric pressure, frequency and intensity of recent events, and the Betterview Hurricane Vulnerability Score
- Wind/Hail Risk: Hail size dossier, historical hail records, damage probabilities based on roof type and condition, and the Betterview Hail/Wind Vulnerability Scores and Claim Predictors
- Flood Risk: First Floor Height Data, satellite imagery, interactive flood maps.
(Peril data partners through Betterview Partner Connect)
These combined comprehensive insights not only help you identify exposures and reduce losses, but also empower you to be an insurance hero to your customers. If a property has insufficient defensible space and is in a historically wildfire-prone area (high wildfire hazard score), you might decide to raise premiums or recommend the policyholder trim vegetation overgrowth. A property located in the same area but with sufficient defensible space and no yard debris, on the other hand, is considered lower risk and you might offer premium discount instead of declining coverage.
These savings and efficiencies, beneficial for both insurer and insured, are possible with our trusted peril data partners, available on the Betterview platform via Partner Connect.