How Big Data Can Quantify Community Social and Climate Risks - from neighborhoods to country levels

Currently, community risks consider only a few parameters such as extreme weather, employment rates and business outlooks. The current practice is a singular standard ESG score for a country or region, ignoring significant sub-national spatial differences or differential importance of ESG factors in estimating the risk profile. The result are general assessments that lack the precision of more granular/investment-grade analyses. But, the world of big data opens up the opportunity to consider other factors and in real time that indicate equally important threats to community wellbeing.

Poverty rates, education levels, food insecurity, English fluency, greenspace, medical However, ESG factors span a wide spectrum of issues that traditionally are not part of common financial analysis despite the risks posed to an organization’s sustainability. A significant obstacle to the widespread adoption of ESG has been a lack of data and the necessary tools to integrate disparate (incomplete) data from multiple sources.

The current practice is a singular standard ESG score for a country or region, ignoring significant sub-national spatial differences or differential importance of ESG factors in estimating the risk profile. The result are general assessments that lack the precision of more granular/investment-grade analyses.

Join SSF and Chatmine Technologies in a demonstration of the Chatmine application, RiskMetrix4ESG, which transforms current up-to-date geospatial data from emerging multiple remote sensing, census, other structured and unstructured data sources into quantitative ESG risk metrics and maps at sub-national (state or metro) scale. Chatmine can generate much more accurate ESG measures and a sounder basis for risk-based decisions and investments. In this webinar, we review the RiskMetrix4ESG methodology and present several case studies using the application.