Corporate performance improvement aligns with ESG metric tracking
More and more, businesses are recognizing that incorporating Environmental, Social, and Corporate Governance, or ESG, into their products and services directly correlates to financial results. Doing good is no longer at the expense of doing well. For both stakeholders and customers, ESG represents another point in their decision making. As allocators of capital, financial services firms play a critical role in shaping a more sustainable and equitable future.
For all the volatility in 2020, one bright spot was that financial services firms have further embraced ESG as a revenue opportunity with regulatory importance. Per an FT.com report, ESG exchange-traded funds, or ETFs, doubled globally in 2020 to over $120B. Similarly, investment banks are helping companies issue green bonds at a record pace. ESG represents significant business opportunities across the Buy-side and Sell-side.
Looking Good versus Doing Good
Quantifying and verifying performance around ESG metrics is difficult and requires AI and ML to spot trends, help determine materiality, and uncover value. Therefore, the foundational building block of ESG starts with data engineering.
I had the opportunity to discuss this in more detail with Junta Nakai, Global Industry Leader of Financial Services and Sustainability at Databricks. Databricks is a data engineering, science, and analytics company and a partner of Microsoft. As Nakai put it, “Eighty percent of the data used for ESG is non-numeric. It’s unstructured data — text, images, IoT, streaming, and other complex datasets. To compound that, there are no standards for this data.”
The lack of standards is certainly worth examining. Today, there are no enforced standards or accountability when it comes to ESG disclosures. It is incumbent upon a company to figure out what, how, and when to report their ESG goals and metrics. Once that is done, the banker, analyst, or investor needs to make sense of what’s been reported. As a result, both financial services firms and the corporates they work with or invest in have a blind spot when it comes to ESG analysis.
When quantifying ESG, financial services firms suffer from data quality and availability issues. The primary source of ESG data today typically comes from self-disclosed annual sustainability or quarterly data vendors’ reports and rankings. This means ESG data isn’t the same as the real-time and reliable market data that powers the rest of capital markets.
Many of these complications can lead to something called “green-washing,” where a company does report out on an ESG metric, but largely through marketing spin, rather than with tangible results or progress. For firms to successfully incorporate ESG into their products and services, data and AI must play a central role in collecting, verifying, and analyzing ESG performance.
In other words, ESG is best addressed today as a data and AI challenge.
Accelerating ESG solutions
You can’t solve a modern data problem like ESG with legacy, on-premises technologies. It requires managing large, unstructured data sets that include things like social media, satellite imagery, and product reviews. You need the cloud to scale, conduct analysis, and draw insights from these vast datasets.
To help address this challenge, Microsoft has been partnering with Databricks. Working together on a common set of principles, we have created Solutions Accelerators to jumpstart a firm’s analytics capabilities.
These Solutions Accelerators are meant to help a firm drive impactful messaging around ESG, productize sustainability, and take a more data-driven analytical approach to understanding ESG performance across their portfolio.
For example, an asset manager can use a Natural Language Processing (NLP) notebook to analyze text from disclosures, helping to solve the problem of analysis against non-numeric data. The NLP can sort through the marketing language and self-disclosures against newsfeeds to bridge the gap between what a corporate is saying and what it is doing, reducing the amount of “green-washing” taking place. This notebook takes ESG further by tying sustainability back to core functions like risk management.
“Together with Microsoft, we can offer a novel approach to sustainable investing by combining natural language processing (NLP) techniques and graph analytics to extract key strategic ESG initiatives and analyze their impact to market risk calculations.” says Antoine Amend, Technical Director of Financial Services at Databricks.
By leveraging these Solutions Accelerators, the firm can operationalize ESG analysis into the products that they offer.
From digital to sustainable transformation
Over the next few years, Nakai believes that digital transformation will give way to sustainable transformation, and I agree. As I’ve previously written, capital markets firms have a unique two-sided perspective on ESG that can help transform the world. Long-term thinking past the next quarter or annual report is critical to understanding a business’s resilience, and ESG plays an important role in that analysis.
The analysis that capital markets firms do will cut the wheat from the chaff. As investors become more ESG-savvy, they will ask questions around a firm’s own ESG stance. Just as customers come to Microsoft’s cloud because of our own sustainability efforts, investors will look towards financial services firms and ask, “If I’m going to be investing in ESG, what is your own stance?” The answer will come in the data.
