Using AI to Achieve Environmental, Social and Governance Goals

Environmental, social and governance (ESG) investments have fast become an important area of interest. It was estimated that sustainable investments amounted to some $30 trillion in 2018, up by 34 per cent from 2016. Indeed, investors (and our societies in general) are increasingly keen to understand whether and by what means businesses are being environmentally and socially responsible and governed. Simultaneously, boards and managements have become cognisant that ESG is crucial to the long-term survival of their companies. Small wonder, then, that some 90 per cent of investors globally already have in place, or have plans to develop, specific ESG investment policies. To guide the selection of such investments, a number of ESG-based rating and index services including MSCI, Bloomberg and Sustainalytics have proliferated in recent years.

Booing at ESG

Unfortunately, ESG investments are often easier said than done. Consider the example of UK-based company, Boohoo. This pioneer of the ultra-fast-fashion retail phenomenon was found to be sourcing from a factory in Leicester in which workers were being paid as little as £3.50 an hour (compared to the National Living Wage of £8.72). Equally bad is the fact that workers were not provided with proper protective equipment against Covid-19. Yet, Boohoo had received a double A ESG rating from MSCI — its second-highest ranking — while being awarded a far-above-industry average score on supply-chain labour standards in its ESG ranking. MSCI is not alone, however. A review of nine other different ratings placed Boohoo in the top 25th percentile of more than 19,000 companies considered worldwide.

Shortcomings of ratings

How could the rating companies have got it so wrong? The answer: all the parties below face different issues with information.

  • Rating and index producers – As a start, each uses its own proprietary methods and data. For instance, they use different ESG definitions, measurements and weightings for different indicators. Furthermore, they rely heavily on information provided by the companies being rated, creating potential bias.
  • Investors – Without standardisation across ratings, it is difficult for investors to compare across indices created by different providers. In addition, they often struggle to determine which rating or score would meet their own investment criteria or goals.
  • Rated companies – As the rating criteria and dimensions are determined by the providers, those companies being rated are frequently left wondering how to improve their own. There is also uncertainty over whether investors have enough information to recognise other positive factors or negative ones related to their competitors which are unaccounted for in the ratings.

In short, the problems emerge from a lack of clarity, consistency and transparency of ESG ratings as well as information asymmetry and shortage.

AI to improve ESG

A possible way to mitigate these issues is to collect more qualitative information to reinforce the quantitative data already in use, with a view to setting up key inputs that could be used as the basis to form common minimum standards. Our AI consultancy Nexus FrontierTech has established a new initiative in Singapore to fashion AI into a tool to collect and process qualitative data. Our solution seeks to help stakeholders mine the vast amounts of qualitative, unstructured data through automation. Until now, gathering and gleaning insights from social media, daily local news, and newly available reports has been a slow and labour-intensive activity, fraught with inaccurate results.

AI technology is a potential game-changer that allows for the speedy collection and handling of information. Algorithm-driven systems can easily crawl and scrape unstructured company data from a range of sources, and subsequently parse and convert this into usable structured data. This, in turn, allows for a curated output that is valuable for all parties involved.

Investors can hence better comply with ESG requirements and make more informed decisions by incorporating ESG data into their investment strategies, for example by implementing negative/positive screening. On the other hand, rated companies are in a better position to identify and control ESG-related issues and risks such as improving their supply chain due diligence. As for the rating and index providers, real-time signals can offer early warnings and timely indicators, enabling them to produce more accurate updates. Furthermore, these providers can expand the scope of analysis using AI-derived information to complement their current quantitative methods.

While this new initiative is still in its nascent stage and the results remain to be seen, history is filled with examples of how technologies can help attain social goals. AI should be no exception.

This article is also featured on LSE, and is written by Danny Goh, Mark Esposito, and Terence Tse.

Danny Goh

Serial entrepreneur and an early-stage investor, co-founded and been CEO of Nexus FrontierTech, investing in early-stage start-ups with 20+ portfolios; currently serves as an entrepreneurship expert with the Entrepreneurship Centre at Said Business School, University of Oxford.

Mark Esposito

Professor of business and economics at Hult International Business School and at Thunderbird Global School of Management at Arizona State University; a faculty member at Harvard University since 2011; a socio-economic strategist researching the Fourth Industrial Revolution and global shifts.

Terence Tse

Professor at ESCP Business School and a co-founder and executive director of Nexus FrontierTech, an AI company. He has worked with more than thirty corporate clients and intergovernmental organisations in advisory and training capacities.

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