A big challenge to successful environmental, social and governance (ESG) investing is the ability of portfolio managers to use data efficiently to drive value creation.
Today, any ESG manager has to access, understand and analyse vast amounts of data, from worker safety standards to greenhouse gas emissions, to meet their commitment (and investors' expectations) to embed ESG analysis into their portfolio management.
This need to meet investors' expectations has placed pressure on research providers who are expected to mine quantities of information to deliver timely, reliable and comprehensible ESG data.
As more companies disclose data using different metrics and frameworks, investors are forced to compare and measure vast datasets.
This can also make it difficult to discriminate relevant data from 'greenwashing' tactics deployed by companies.
ESG analysts are forced to overcome logistical obstacles so that they can screen public data and company information to find accurate and timely ESG data.
How can this data overload be tamed? Step forward innovative solutions, such as artificial intelligence (AI), machine learning and satellite imaging. Asset owners are now forced to look beyond traditional datasets and practices.
Objective, scalable and transparent, these techniques can help make ESG data collection into a coherent, unified process.
They allow research providers to improve the quality and rigour of their data, while offering asset managers a platform that has the functionality to improve the granularity and quality of information available to their investors.
Despite the integration of AI within ESG analysis being in its early stages, we are starting to see AI implemented within portfolio construction.
Pioneers such as TruValue labs mine big data and apply AI to their research output to assess, monitor and rate ESG behaviours and criteria, in real time.
There are also examples of big data being used as part of a portfolio's risk mitigation strategy. For example, satellite imagery is being used to monitor for natural disasters, such as flooding.
AI and machine learning enhances the credibility and authority of ESG integration. The software can help avoid claims of 'greenwashing' and ensure ESG analysis remains credible and reliable.
The industry has worked to promote the adoption of ESG criteria and educate investors about the long-term opportunities ESG data presents.