Problem:
Climate change and evolving government regulations fuel a growing demand for reliable physical asset data, asset ownership, and location-specific socioeconomic and environmental factors connected to climate forecasts. Traditional risk measurement methods rely heavily on self-reported data and are no longer adequate to meet customer needs.
Think of your own investments. You have little insight into where these companies operate and potential exposures. SRS provides a data-driven solution to identify and help mitigate these risks.
Opportunity:
While data sources are available, integrated, simple-to-use, and economically viable solutions do not exist.
Challenges:
•No complete or credible Asset Location, Function, or Ownership Source exists.
•The existing practice of using customer-provided data does not support portfolio or sector analysis.
•Ingesting, reconciling, and integrating this data is complex and expensive.
•The execution risk is high because required data governance know-how and operational competencies are expensive to develop or find in the market.
Solution:
Spatial Risk Systems (SRS) is the first spatial-level data aggregator connecting dozens of data sources into a simple turn-key publishing solution for use in Finance, Banking, Asset Management, and Insurance.
Five Major Data Dimensions
1) Asset Location, Function, and Ownership with links to Financial Identifiers for simple data ingest
2) Climate/Physical Risks - Historical Losses, Expect Annual Losses, Forward-Looking Climate Intelligence providers
3) Carbon/Methane Emissions—the most complete global source of facility-level emissions. There are 13,000 facilities and 3,000 asset owners—partnerships with Satellite Methane Emission Providers.
4) Environmental - Air and Water Quality Measures, Discharges to Water, Toxic Release, EPA Reporting and Fines
5) Socioeconomic - Local demographics: Income, Poverty, Age, Race, Education, etc.
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