Key benefits of 7A for insurers & banks

  1. Ground-breaking pluvial flood risk model for individual buildings. 
  2. + 250 physical parameters modelled for high–resolution risk understanding.
  3. Adaptable with insurers own models and claims data for higher accuracy.

Linking geo and insurance data

 

A building’s surrounding environment can be parameterized based on their location with respect to landscape slope, height, location in catchment, distance to streams as well as build-up area and infrastructure.  

 

7A for insurers and banks numerates hundreds of unique parameters that affect flood risk. Through repeated iterations and claims data training, we have greatly refined the modelling approach. 

 

This is done at an unprecedented resolution and depth of risk analytics. 

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Portofolio distribution according to risk categories. Flood claims data are used as basis for machine learning to find data patterns and correlations and learn from historical events to predict future risk.
Digital Elevation Models and other crucial data layers are continuously updated to match urbanization and climate change dynamics.
Land use and other data streams are carefully reprocessed to fully capture risk dynamics

Data for pricing risk management

 

Flood risk differs even between properties on the same street. The ability to capture underlying risk differentiators is a question of data quality, modelling power and flood insight.  

 

After having applied our models across three continents and numerous of insurers, claims patterns have emerged. These patterns can inform existing price and risk models at very detailed levels to ensure competitive pricing and sustainable operations even in a risky climate. 

Flood risk may vary from one side of the street to the other with historic claims only telling a partial story.

Turning from model- to data-centric

 

Past floods can no longer provide the flood risk of the future.  Shifting weather patterns are making floods more unpredictable, bringing them to previously unaffected areas and increasing the frequency in regions once considered low-risk. 

 

Model-centric datasets rely heavily on historical data. They will not accurately capture future risks—highlighting the need for a different approach. 

 

A data-centric approach allows for the development of specialized datasets that reveal the complexities of flood vulnerability. By analyzing hundreds of physical parameters for each building, 7Analytics applies natural science insights to identify why a building may or may not be at risk, offering a precise understanding of flood risk on an individual level. 

Losses weigh in heavily on the high flood risk categories of 7A for Insurers & Banks.

Get in touch!

Are you interested in learning more about 7Analytics? Please get in touch or request a demo.

Sinah Truffat

Head of Sales

st@7analytics.ai