A geospatial asset management model for physical and transition risk assessments and capital reallocation (GeoAM2)

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GeoAM2 is an innovative approach to seamlessly integrate climate risk assessment into asset management sustainability practices. Specifically, the innovation lies in its predictive risk assessment of its assets as an early-warning signal giving managers early indications of when intervention may be needed. GeoAM2 integrates spatial finance into its analytical framework to evolve accurate representation of how vulnerability and impairments are distributed across a portfolio of assets. Earth observation and remote sensing combined with machine learning have the potential to transform the availability of information in our financial system. It will allow financial markets to better measure and manage climate-related risks, as well as a vast range of other factors that affect risk and return in different asset classes.GeoAM2 is thus a disruptive approach to asset management that seeks to achieve three things: (1) aggregate assets owned by asset management firms into a database with extensive unique nameplate data; (2) geolocate and tag these assets to determine countries or regions of existence; and (3) evolve immediate and predictive asset risk assessment scores using integrated AI and ML algorithms. GeoAM2 has in place the sub-components of investments held by asset/wealth management firms and in real-time and provides two risk scores (immediate and predicted) to managers highlighting and predicting how such risks will change based on projected government policies. This tool as an early-warning environmental risk assessment tool ensures that managers can quickly diversify investments and avoid losses occasioned by huge environmental/climate penalties due to poor portfolio management strategies.