ESG: Seeing the wood for the trees with the help of AI

By Diana Rose, ESG Research Director at Insig AI

As the demand for sustainable investing continues its rapid growth, so too does the importance of reliable ESG credentials and analysis. The uncomfortable truth is that not all ethical investments are what they appear to be, and it’s easy to lose sight of key ESG issues in decision-making. The challenge for investment managers, therefore, becomes how to truly see the wood for the trees.

At its heart, the rise of ESG is a response to demand for companies to do what’s right. If this seems hard to pin down, it is because it is – what’s right for one corporate now may not be right for others in the future. To find consensus and gain perspective on the big picture, while also gathering the detail that really matters, are real challenges that threaten ESG’s credibility. They are also where AI and machine learning can play a crucial role.

Traditionally, ESG credentials are based on the findings from a human analyst, meaning they can be skewed by bias and assumptions. These findings are then prone to being highly processed by obscure methodologies to deal with the challenge of aggregating disparate data. Meanwhile, to add a further challenge, the goalposts of what matters to whom are constantly shifting.

Furthermore, investment analysts are facing an ever-increasing amount of complex data and information relating to ESG. Gathering reliable data points, unpicking methodologies and reading between the lines of Sustainability Reports can seem like a monumental task. Going deep is one thing, but after all that hard work, data can still end up meaningless unless seen in the wider context of a decent benchmark.

This is where the ratings agencies such as MSCI and Sustainalytics stepped in to do a lot of the hard work of research, input, benchmarking and scoring. Asset Managers breathed a sigh of relief. But it soon emerged that a disparity in approaches was resulting in a lack of consensus and transparency in the outputs.

This was recently highlighted by analysis by State Street Global Advisors, as part of an 18-month due diligence process which looked at 30 ESG data providers. It found that ratings aligned on only half of the companies covered. As SSGA noted; “in choosing a particular provider, investors are, in effect, aligning themselves with that company’s ESG investment philosophy.”

One thing has become clear: a simple ESG rating is no longer enough for asset managers to hang their hats on; proprietary analysis is still crucial in the quest for evidence and transparency to underpin their confidence in ESG investment decisions.

This is where AI and machine learning can add real value to the ESG analyst’s task as well as the investment manager’s confidence. When it comes to drilling deep into a company’s ESG disclosures, for example, machine learning can do in minutes what would take a team of people days.  And as for going wide, AI is perfectly equipped to filter, organise and benchmark huge volumes of diverse data in ways an analyst can then digest and explore. AI can do a lot of the hard work, without introducing the same human bias and assumptions that can creep into aggregation methodologies.

Natural Language Processing (NLP) is a great example of AI’s ability to go wide, by harnessing the content of hundreds of thousands of sources for relevant data. NLP can find and contextualise text that is not in a structured format and make sense of it. The result is that relevant insights from live, ‘real world’ sources can be organised, ready for analysis alongside more formally reported information. This provides a new level of context and richness to the picture of an ESG investment opportunity. NLP can also act like an ‘ear to the ground’ in a shifting landscape of ESG materiality and risk, identifying and highlighting emerging trends that may affect a company – beyond what they’ve reported or are even aware of.

While we are still in the early stages of AI’s application to ESG, some key factors are set to rapidly accelerate its use. On climate change, for instance, it’s no huge surprise that some companies have been getting away with greenwashing – for example talking about ‘Net Zero’ without any strategy to get there. AI has the ability to help bring this to light, and public, political and regulatory scrutiny is on the rise; as Raba Foroohar recently pointed out in the FT “individual companies are being held explicitly responsible for the risks of global warming”.

These increasing standards and scrutiny are both overdue and necessary, making it an exciting time in the world of ESG. But the speed at which things are moving presents challenges for both companies and investors, and there’s a risk that transparency, evidence and ultimately credibility get compromised. As a partner to human decision-making in a complex and at times perplexing world, the power of AI is starting to be harnessed for ESG; if done right, it will add both quality and that all-important sense of perspective.