By using AI, the solution removes human biases and delivers an accurate and actionable outcome/score at a zip code or municipal or regional level.

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ESG Analytics

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

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Poverty rates, education attainment, food insecurity, English fluency, greenspace, health and wellness, air and water quality, and other ESG (Environment, Social, and Governance) factors can now be captured at the neighborhood level and are used to evaluate the stability, resiliency, and economic vitality of a community.  The application of big data at the neighborhood level is an emerging field of analysis.

 Who should care? Municipalities, investors, insurance, philanthropic, and other organizations routinely rely on risk analysis to make planning, investment, risk, and community wellbeing decisions.

 Join SSF and ESGAnalytics.Ai Team, Gopal and Pitts, in a demonstration of their analytics using three case studies. Andrew Teras and Michael Bonanno, senior credit analysts from Breckinridge Capital Advisors, serve as panelists. They discuss how they used ESGAnalytics.Ai to evaluate the risks in their municipal bond portfolio.

Webinar Part 1 – Intro

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