Demand for data screening services that can help clients find investments that meet environmental, social and governance (ESG) standards has skyrocketed in recent years.
More than 20 percent of global fund assets under management were invested using at least one restriction screen (which allows investors to avoid certain sectors or companies) at the end of June 2023 – ten times more than three years earlier, according to a report from Morgan Stanley.
As Morgan Stanley researchers note, demand for more ‘exclusionary’ investments has increased not only in response to changing investor values, but also as a result of rapid regulatory changes – such as the EU’s Sustainable Finance Disclosure Regulation, which stipulates the mandatory ESG information that asset managers must provide.
The ability to screen investments means that investors are able to not only steer clear of, for example, arms manufacturers or thermal coal or tobacco companies – which are the most common exclusions – but also invest in ways that highlight broader beneficial outcomes, such as cleaner energy or gender equality.
Morgan Stanley estimates that approximately 8 percent of global fund assets are now sustainably invested.
How is the screening carried out?
Index and data providers agree that they would not be able to meet the explosion in demand for ESG screening and rankings if there had not been a parallel leap in the capabilities of the technologies to provide them.
Morningstar Sustainalytics, one of the best-known providers of ESG data and indices, says it uses information retrieval and extraction technologies, along with various types of artificial intelligence – such as machine learning, natural language processing and symbolic AI (which tries to imitate human capabilities). to deal with concepts and rules of conduct) — to screen investments against hundreds of ESG criteria.
This means, the report says, that it monitors hundreds of thousands of publicly available sources, starting with disclosures published on websites, media publications, publications from regulators and non-governmental organizations, and providers of non-standard data, such as independent research into supply chain risk .
“In all cases, there is an element of curation, where our analysts monitor the output of the automated process to ensure the highest quality standards are met,” said Arik Brutian, senior vice president of digital innovation at Morningstar Sustainalytics .
Vinit Srivastava, managing director and co-founder of MerQube, a specialist index provider that has entered the ESG sector, points out that the amount of data has ‘increased’ since interest in investing according to sustainable principles has gained ground.
This proliferation and the resulting confusion and contradictions that come from trying to make sense of different data sources means that there is a greater need than ever for what he calls “prompt engineering,” used in generative AI models. “It’s very important in all of this,” he says. “The questions you ask generate the answer you get.”
Srivastava notes that any information that is difficult to interpret, however, presents an opportunity for any investor looking for a competitive advantage. One of the niche areas MerQube works with uses natural language processing to analyze the language used by senior executives on earnings calls, which, he says, are forward-looking, unlike earnings reports that provide a historical view.
“Technology has made this possible,” says Srivastava.
What are the technological challenges in screening ESG data?
Morningstar’s Brutian says one of the biggest challenges for those screening investments against ESG criteria relates to the quality of the original data. “The quality of publicly available data does not always meet our expectations, and ESG data disclosure is not yet standardized,” he said, adding that Morningstar Sustainalytics has created technology-enabled safeguards to try to ensure data reliability and guarantee reliability of sources.
Other challenges relate to the speed at which regulations change. A timeline for the implementation of sustainable finance provided by the European Securities and Markets Authority indicates more than that 20 installments regarding requirements for new disclosures between early 2021 and 2028.
Every time a law changes, the data screening database must also change. Government authorities may also impose sanctions or restrictions in some cases, for example on Chinese suppliers linked to military institutions or forced laborers in the country’s Xinjiang region.
For this type of screening, there is no substitute for the in-depth shoe leather research of traditional risk consultancies, even if supplemented with natural language processing and machine learning. “Without technology we couldn’t do half of what we do,” says a specialist supply chain screening expert. “But there is no substitute for smart people.”
Data screening professionals have also had to develop ways to compare data over time, for example a company’s promises on climate goals. Morningstar Sustainalytics says it uses processes that allow it to “triangulate” targets reported by companies and compare them with those companies’ emissions data.
Leonardo Bonanni, founder and CEO of Sourcemap, a specialist in supply chain data, says its systems can check for fraud. For example, if a company claims it uses recycled materials, Sourcemap can verify that there is transaction data to support this.
This work has been greatly aided, Bonanni says, by what he calls “robust AI” technologies such as natural language processing, rather than the generative AI technologies such as ChatGPT that have recently made headlines. Srivastava agrees. “AI techniques are not new; what has changed is the processing power,” he says.