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The future of finance goes hand in hand with socially responsible investing, environmental stewardship, and corporate ethics. In order to stay competitive, Financial Services Institutions (FSI) are increasingly disclosing more information about their environmental, social, and corporate governance (ESG) performance. Hence the increasing importance of ESG ratings and ESG scores to investment managers and institutional investors. In fact, the value of data-driven ESG global assets has increased to $40.5 trillion in 2020.

With nearly ⅓ of global managed assets focused on ESG, it’s clear that the broad benefits of incorporating ESG goals and responsible investing are well understood by companies, investment professionals, and regulators. But how do you harness the insights buried deep in non-financial data sources from CSR reporting and social media to impact investment and carbon offsetting strategies?

In the recent technical workshop “Data + AI in the World of ESG”, that attracted some of the biggest financial services institutions in the world, we aimed to answer that question by educating the community on how data and AI can help organizations better understand and quantify the sustainability and social impact of any investment in a company. Turns out, although many companies are prioritizing ESG factors as a strategic initiative, many of them don’t have the resources and data strategy to really take it to the next level.

In fact, when asked about whether they have technical resources focused on ESG, only 31% of respondents said they had dedicated data engineers and data scientists for ESG. And when asked whether their ESG strategy leverages data and AI, less than a third of respondents said yes.

To help the attendees address some of these problems, Junta Nakai, the industry leader for Databricks’ financial services business provided an insightful overview of the connections between companies and how understanding the positive or negative ESG consequences of these connections may have to one’s business and investment process and strategy.

Joining Junta was Antoine Amend, technical director, who dove into how Databricks can enable asset managers to assess the sustainability of their investments and demonstrated ways to use machine learning to extract the key ESG initiatives, as climate change mitigation, as communicated in yearly PDF reports and compare these with the actual media coverage from news analytics data.

Through this novel approach to sustainable investing and asset management, companies can combine natural language processing (NLP) techniques and graph analytics to extract key strategic ESG initiatives and learn companies’ relationships in a global market and their impact on market risk calculations.

How to Use ML to Extract ESG Insights for Ethical Investing
If you missed the workshop or would like to share the contents with your colleagues, you can access the replay below.

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Try the below notebooks on Databricks to accelerate your ESG development strategy today and contact us to learn more about how we assist customers with similar use cases.

  1. Using NLP to extract key ESG initiatives from PDF reports
  2. Introducing a novel approach to ESG scoring using graph analytics
  3. Applying ESG framework to market risk calculation


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