ESG Investing  

How machine learning is being used to combat greenwashing

  • Describe some of the challenges of ESG investing
  • Explain how machine learning can help
  • Describe some of current thinking about ESG investing

What does AI bring to the world of ESG rating?

How then can Artificial Intelligence (AI) algorithms do a better job in spotting and objectively analysing ESG risk? First, given the poor availability of ESG data (and the danger or misreporting), AI offers a powerful approach to data acquisition and validation. It is capable of scouring thought leaders’ comments on social media, influencers such as Amnesty International’s red-flags, as well as news flow on events and sentiments. 

You can therefore eliminate a reliance on either what a company says about itself or what its management tells a human analyst.

Second, ratings are often criticised for inconsistencies over time, sector, country and more. Machines are inherently objective and can be scaled across vast investment universes. This makes AI-led ESG risk assessment a compelling solution in principle. 

Algorithms will, in the future, be capable of being run over all the available structured and unstructured information linked to tens of thousands of listed businesses, tirelessly uncovering developments worthy of analysing and scoring, all in a consistent and objective manner.  While there are huge challenges to developing AI in this area, the current alternative (human analysts) are expensive, inconsistent, and necessarily subjective. 

ESG Risk or Responsibility? 

Let’s look at a typical approach of one major US investor today, the Californian State Teachers Retirement System (CalSTRS) . CalSTRS is the second largest pension fund in the US with Assets Under Management of £283bn. Last year, it decided that a small proportion of its investments must be net zero. They have established a roadmap for their investment managers whereby that percentage must increase steadily year on year. 

So not only must the investor’s investment managers meet that percentage target, but they would also be wise to increase weightings in companies which have set fairly aggressive net zero emissions targets. However, this narrow focus on Green House Gas (GHG) emissions may not extend to covering other legitimate ESG targets such as over extraction of water in manufacturing or micro plastics pollution. 

However, if as an investment manager you go beyond these highly-focused remits into other ESG- related targets that are not a priority for the asset owner and that are perceived to limit investment returns, you risk losing your mandate. It’s a balancing act for asset managers – you cannot afford to make too many changes too quickly. 

Asset managers must move at the pace of the investors, working with their changing ESG priorities. However, it’s critical to be able to tailor ESG measurement systems to investors’ bespoke demands and focus areas.