AI can help tackle ESG data challenges in sustainable investing
Efforts are afoot among financial institutions to present quality, standardised and simplified data to support investors’ goals.
WITH evolving regulations and discourse around sustainable investment in many parts of the world, more investors are beginning to understand the relevance and importance of sustainability as part of their investment goals. This is especially among the young and affluent.
However, many investors are unable to properly pinpoint a company’s environmental, social, and governance (ESG) performance because of the lack of standardised ESG information.
Today, it is difficult for investors to compare companies’ ESG performance as there are different methodologies, metrics, and weightages – adding complexity for investors to make informed decisions.
Additionally, sustainable investing means different things to different investors – depending on their approach, values, interests, and principles.
Combating scepticism with data
Up until a few years ago, investors relied on general ESG scores for their portfolio decisions. Today, investors are asking complex questions: Are ESG ratings multi-faceted, and how robust is the data behind those scores? They want to know if companies are looking beyond traditional risks to consider if the investment objective has a positive impact on the world and addressing material environmental and social risks.
Financial institutions have increasingly turned to data to communicate the beneficial impact of sustainable products to investors. In fact, there is potential for US$8.2 trillion of retail investor capital to flow into sustainable investing across Asia, Africa, and the Middle East by 2030, based on Standard Chartered’s Sustainable Banking Report 2023.
Of this US$8.2 trillion, it is estimated that US$3.4 trillion could be mobilised specifically towards climate mitigation and adaptation, with 93 per cent of investors surveyed signalling interest to embrace climate investing. This demands a data-driven approach to ensure trust and transparency, especially since greenwashing concerns are on the rise.
Whether channelling funds into renewable energy initiatives, electrification of transportation businesses, or supporting social enterprises fostering fair and equitable employment in developing markets, data is the pillar to uncovering opportunities to make gains socially, financially, and environmentally.
Standardising the disclosure of data
Around the world, the push for standardised data in sustainable investing is fuelled by regulatory compliance requirements.
Governments are concerned about data transparency and quality and are calling for companies to be accountable. This has resulted in a growing number of governments putting in place strict mandates which demand comprehensive disclosure of ESG data.
A prominent example is that of Europe’s Sustainable Finance Disclosure Regulation (SFDR), which requires financial market participants to disclose various sustainability aspects of their market offerings. In Asia, Singapore and Japan too have enacted regulations on ESG disclosures for listed companies.
These initiatives are a step in the right direction, reflecting a global trend towards standardised reporting, catalysing the adoption of consistent data frameworks in sustainable investing practices. But there is more that can be done.
How AI can help
AI capabilities can be helpful in searching for and synthesising useful ESG data for sustainable investing. This helps meet the growing investor and consumer demand for information on how companies operate, and the sustainability aspects as key factors in buying and investing decisions. Particularly for investors, they are on the lookout for companies adopting practices that will mitigate risk and ensure long-term sustainability.
Advances in AI have also made it easier than ever before for computers to automate complex tasks at incredible speeds and volumes, revolutionising how financial institutions work with ESG data. Supported by machine learning, robotics, and AI, the technologies help financial institutions to gather, locate and analyse data according to pre-determined specifications.
AI can analyse data from various sources such as news articles, research reports, videos, and social media posts. This analysis can provide insights into public and investors sentiment, risks to reputation, and emerging trends related to ESG.
The power of AI and other disruptive technologies can also be harnessed to accelerate ESG investment goals such as advances in climate modelling and simulation, tracking of increased equitable employment among women in developing markets, or climate change’s impact across a wide range of assets. This improves the speed and search of data, helping investors to make informed research in funds and companies, and ultimately their investment decisions.
While data quality remains an issue, AI can contribute towards solving some of the other data challenges, especially in terms of processing and sieving through large chunks of environmental and social data as more and more companies commit to increasing disclosure.
Sophisticated data capabilities become important beyond meeting sustainable investment goals but also to maintain an edge on strategic investment allocation and performance outcome.
The writer is global head, sustainable finance, wealth and retail banking at Standard Chartered Bank
