ESG needs to be rethought: technology holds the key
Regulation will change the ESG landscape
A range of regulatory interventions have been highlighted in recent months – from Sustainable Financial Disclosure Regulation (SFDR) in Europe to regulations aligned by the Securities and Exchange’s Task Force on Climate-Related Financial Disclosures (TCFD) Commission in the United States. However, the elephant in the room is: how do you truly harness the potential of ESG? To understand the problem, we need to dig deeper into how ESG is mainstreamed today by companies and investors, and where artificial intelligence can ultimately help.
Key issues plaguing ESG ratings and the investment world:
- Regulation: ESG regulations are changing. And quick. The challenge of competing regulations, jurisdictions, interoperability, alignment and more makes late adoption at best when it comes to assimilating policy and regulation in global markets. While companies are still struggling to meaningfully integrate ESG, they are already under pressure to report on the TCFD and soon on the TNFD, the Task Force on Nature-Related Financial Disclosures.
- Data guarantee: Data assurance in ESG integration needs to be addressed urgently and comprehensively. The extent, availability and veracity of underlying ESG data is key to overcoming the problem of greenwashing and the resulting risk to investors. We should properly monitor and validate ESG data, working with their subjectivity rather than ignoring their subjective nature but, in order to deliver meaningful results, in relation to ESG
- Expertise and capacity: There is currently a significant talent gap in the ESG market, which will only get worse as various stakeholders, from businesses and consultancies to ESG rating companies and financial institutions, compete for scarce talent. . Lack of talent ultimately affects the application of ESG and the quality of the underlying data gathered and provided to or by companies. The talent supply will only grow as academic institutions begin to expand their offerings in this space.
- ESG integration: Without the depth of talent and tools required, ESG integration continues to be mostly superficial in many companies. This is in no way helpful, as it only adds to the doubts surrounding the usefulness of ESG. Companies, more than anyone, need to recognize the value of adopting ESG if it is to become truly important the way it should be.
Simply put, the rigor of ESG analysis is a far cry from what is typically seen in areas such as financial analysis or corporate planning. As the CEO of an AI-enabled analytics platform that has conducted its own research and discussion on a cross section of global companies, I believe that if ESG continues to be used by many investment professionals as a marketing tool, this does not always translate into true integration or gain traction with decision-making units in the executive suite and boardrooms. ESG clearly needs to be rethought.
The key to enabling this change will be the selective integration of AI technology into current ESG processes and methodologies. Major ESG players have experimented with using AI to improve their ESG rating results. But the nebulous nature of ESG aspects continues to make this very difficult, if not impossible, with traditional machine learning (ML) approaches. The problems described above only add to the challenge.
Where is a good place to start with ESG?
A good starting point would be a calibrated approach involving a judicious mix of human intervention and capability, supported by AI-based tools that will deliver the most practical and meaningful results. This will enable the “quality-assured scalability” that is needed to fundamentally improve ESG integration, minimize greenwashing and deliver on the promise of ESG.
Emphasis will also need to be placed on leveraging AI in areas such as rapid data aggregation, data quality assurance, analytics and intelligent reporting to achieve efficiency and effectiveness. efficiency. Additionally, applying AI with data aggregation based on environmental sensors could provide a critical data aggregation mechanism, which could then be integrated into the business end of the spectrum.
Using AI through a combination of these steps will help drive deep and meaningful business integration and related analytics based on models designed bottom-up to deliver actionable results. And with that, times may actually be changing for the better for ESG.