Developments in Artificial Intelligence (AI) and machine learning have led to the creation of a new type of ESG data that do not necessarily rely on information provided by companies. If ESG data provided by rating agencies is essential, there are also some concerns surrounding the quality of this data.
Firstly, there is a high degree of subjectivity in the choices made by the rating agencies on ESG criteria — they rely heavily on information provided by the companies they rate.
Secondly, companies’ ESG ratings are reviewed infrequently while the direction of revisions tends to be strongly correlated with financial performance. Large discrepancies among the agencies’ ratings can also occur, partly due to the different methodologies used to deal with missing data. These can be large, but, interestingly, research has established that greater ESG disclosure actually leads to greater ESG rating disagreement.
The potential of AI
The good news is that AI tools are available now which can collect and analyse more information on ESG risks and opportunities than ever before. These tools improve the quality of data and create new exciting opportunities.
The benefits of AI in ESG investing include providing textual analysis to measure companies’ ESG incidents and commitments, collecting satellite and sensor data to determine environmental impact, and physical risk exposures. AI can also help bridge the gaps in company data.
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Textual analysis can identify companies’ controversies and important ESG news. ESG data providers can use Natural Language Processing (NLP) tools to analyse real-time company information to measure controversies surrounding environmental policies, working conditions, child labour and corruption, among others. To illustrate this, RepRisk, an ESG data providers, analyses more than 80,000 media, stakeholders and third-party sources daily, detecting incidents that occur in companies’ ESG policies. This type of analysis can be very informative, adding value to ESG investment processes.
There has also been a remarkable increase in satellite and sensor data in the recent years. Possessing a wide geographical coverage, this data can be used to verify companies’ carbon emissions, or to analyse their impact on ecosystems: air pollution, waste production, deforestation and floods. This type of data can also be a key ingredient of climate risk stress testing models, the findings of which have been very informative.
AI can help bridge the gaps in corporate disclosures. While it is considered mandatory for large companies to report on Scope 1 and 2 greenhouse gas (GHG) emissions, reporting on Scope 3 emissions (indirect emissions that occur in a company’s value chain) is optional. However, while Scope 3 emissions can often be the largest component of companies’ total GHG emissions, they are rarely publicly available.
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To date, data vendors have relied on simple regression models to predict the likely GHG emissions of some companies. A recent study used statistical learning techniques to develop models to predict such emissions based on publicly available data. This approach generated more accurate results than previous models.
AI enhances ESG data but is not without drawbacks
Ratings based on NLP signals can become public “sentiment” indicators, particularly when the primary source of data comes from social media. NLP signals tend to overweight certain key issues, in that it generates the most ESG controversies, and weights can fluctuate a lot through time.
Company disclosures can also be subject to manipulation as communication is increasingly reshaped in light of AI algorithms. Managers can learn to avoid words that could be perceived as negative while favouring language preferred by ESG algorithms. Another issue is a lack of historical data in some instances, which might lead to biases and representatively issues.
The future of AI in ESG investing
AI has the potential to contribute notably to improving the monitoring of ESG reporting and goals. However, there are still challenges in analysing the extensive data available while the choice of one measure over another could have a large impact on the outcome.
In the end, a comprehensive investment process should avoid placing too much confidence in a single measure. Furthermore, one also needs to consider the costs of maintaining alternative datasets: not only the costs of acquiring data, but also the investment required to store and integrate these large datasets, activities that might necessitate a dedicated team.
Overall, the common consensus is that ESG integration into investment approaches will become more profound and the ability to use robust data will play a major role in that process. Not only can AI help extract relevant information from existing data sources, it also offers exciting opportunities to create new ones.
Marie Brière is head of investor intelligence and academic partnerships at Amundi Institute, affiliate researcher at Paris Dauphine University & Université Libre de Bruxelles, member of the consultative working group of the Financial Innovation Standing Committee (FISC) of European supervisory authority ESMA
