Artificial Intelligence Helps Make Sense of ESG Data

June 25, 2020

Wouldn’t it be nice if your company could prepare an annual environmental report as easily as it prepares a yearly financial one? And wouldn’t it be even nicer to be sure that it is being read and assessed equally with other companies in your field?

Artificial intelligence tools are making this possible.

As investors and stockholders place more emphasis on Environmental, Social, and Governance (ESG) and sustainability ratings, companies are working hard to accommodate.

GreenBiz reported that corporate sustainability reporting has increased from 20% of S&P 500 companies in 2011 to 86% in 2018.

However, many of these ESG reports lack consistency and standardization. Therefore, it is often hard for investment agencies and stockholders to compare the findings, give companies consistent ratings, and make appropriate decisions.

Companies often struggle with what and how much to report, fearing that much of the data may never be read or used. Plus, because of the difficulty to pull the information together, ESG reports are often distributed a quarter or more later than annual financials.

Enter the Bots

Here is where technology comes in.

John Davies, Vice President and Senior Analyst at GreenBiz Group, says in his article, The Bots Are Coming (to Ratings and Reporting), “Subscribing to a service such as CSRHub or Sustainalytics is often more about the data than the rankings. Firms such as these provide data services where software known as APIs can pluck data and populate a firm’s database, where its internally developed algorithms can test and validate various investment hypotheses.”

Other firms such as Sensefolio and Arabesque, scour news reports, social media posts, job postings and review websites such as Glassdoor, looking for data which is then leveraged with self-learning quantitative models to assess the performance and sustainability of globally-listed companies.

Both companies and investment firms are seeing a trend toward the leveraging of automation and AI to generate, as well as evaluate ESG data regarding performance and achievements.

However, “AI works best when humans develop an investment thesis and machines test that theory,” Davies says.

Currently, investors and sustainability teams are working together to define a list of strategic keywords that would help develop more effective searches and analyses.

What Should Companies Do?

If a company is concerned about what ESG data and services firms are picking up, “Regular checkups on a Bloomberg terminal of a company’s publicly available information can help make sure the bots are getting the right data — and getting the data right,” says Davies.

Davies also suggests subscribing to SaaS providers such as Datamaran. These types of firms identify and monitor non-financial risks by sifting and analyzing millions of data points from publicly available sources. The data can then be exported and brought in a risk register or Risk Management software.

Technology is also being applied to internal processes, in addition to external ESG data. For example, Enablon is used by organizations to measure, track, and manage carbon emissions, energy consumption, waste generation, and environmental impacts. Enablon is also used to collect sustainability data and generate sustainability reports.

Software technology, AI, and automation are helping with many aspects of sustainability reporting and ESG ratings. Companies must be sure to follow the trends so they’re not left behind.


Laurie Toupin