Using AI to screen, search, and structure environmental, social, and governance data

Investors face at least two challenges when making environmental, social, and governance (ESG) decisions. One is the discrepancy in definitions, scoring methodologies, and assessments. The second one is the dearth of timely and accurate data. Terence Tse, Marissa Lum, Danny Goh and Mark Esposito write that AI carries the promise to provide an immediate solution to mitigate the data problem.

 
In an earlier article, we explored how artificial intelligence (AI) can help reach environmental, social and governance (ESG) goals. Here we intend to look deeper into the AI technologies and how they extract and handle data to make investors better informed. Such technologies are increasingly, if not urgently, needed as ESG issues are fundamentally changing the investment landscape.

 
Inadequate information

Yet, there are at least two informational challenges that investors face. The first, as we mentioned in our previous article, is that there are discrepancies among rating and index producers – even when scoring the exact same companies. A recent study has found that in a dataset of five ESG rating agencies, correlations between scores on 823 companies were on average only 0.61. The reasons for the inconsistency lie in the differences in definitions, scoring methodologies and assessments used. Investors often don’t have the time to go through, compare and reconcile the differences of views and ratings from different suppliers.

The second is the dearth of timely and accurate information to make informed decisions. Here is an example: does Tesla qualify as an ESG company given that electric cars are purportedly good for the environment? If the answer is yes, what about the fact that the batteries used in Tesla cars depend on nickel, the extraction of which comes at an environmental and health cost? How about the fact that Tesla’s recent purchase of $1.5 billion worth of bitcoins, the processing of which is extremely energy-demanding if not downright wasting?

AI carries the promise to provide an immediate solution to mitigate the latter problem. As machines are much more capable than humans to gather and handle qualitative information at scale, cheaply and rapidly, the supply of such information will in turn improve the completeness and timeliness of data, and hence the overall quality of ESG data available to investors.

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Πηγή: blogs.lse.ac.uk