Regulations will change the ESG landscape
A number of regulatory interventions have come to the fore in recent months – from the Sustainable Financial Disclosure Regulation (SFDR) in Europe to the Securities and Exchange Commission’s own Task Force on Climate Financial Disclosures (TCFD) harmonized regulations in the US. However, the elephant in the room is this: How do we really harness the potential of ESG? To understand the problem, we need to dig deeper into how ESG is being integrated by businesses and investors today, and where AI can ultimately help.
Key issues following ESG ratings and the investment world:
- Regulation: ESG regulation is evolving. And fast. The challenge of competing regulations, jurisdictions, interoperability, compliance and more makes adoption late at best when it comes to assimilating policies and regulations in global markets. While businesses are still struggling to meaningfully integrate ESG, there is already pressure on them to report to TCFD and soon to TNFD, the Task Force on Nature-Related Financial Disclosures.
- Data security: Data assurance in ESG integration needs to be urgently and comprehensively addressed. The volume, availability and veracity of the underlying ESG data are key to overcoming the problem of greenwashing and the subsequent risk for investors. We should monitor and properly validate ESG data, working on its subjectivity, not overlooking its subjective nature, but to provide meaningful results, in relation to ESG
- Expertise and Ability: There is currently a significant talent gap in the ESG market, which will only get worse as various stakeholders, from businesses and consulting firms to ESG rating companies and financial institutions, vie for limited talent. The lack of talent ultimately affects the application of ESG and the quality of the underlying data that is collected and delivered to businesses or companies. The talent pool will only grow as academic institutions begin to increase their offerings in this space.
- ESG integration: Without the necessary depth of talent and tools, ESG integration continues to be largely superficial in many businesses. This is not helpful in any way, as it only raises doubts about the usefulness of ESG. Companies, more than anyone else, need to recognize the utility of ESG adoption if they are to become truly material in the way they should be.
Simply put, the rigor of ESG analysis is far beyond what is generally seen in fields such as financial analysis or business planning. As the CEO of an AI-enabled analytics platform that has conducted its own research and discussions on various global firms, I believe that while ESG continues to be used by many investment professionals as a marketing tool, it does not always translate into real integration or gaining traction with decision-making units. in the C-suite and meeting rooms. ESG clearly needs an overhaul.
Key to enabling this change will be the selective integration of AI technology into current ESG processes and methodologies. Major ESG players are experimenting with the use of artificial intelligence to improve the results of their ESG ratings. But the nebulous nature of ESG aspects still makes this very difficult, if not impossible, through traditional machine learning (ML) approaches. The above questions 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 careful 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’ necessary to fundamentally improve ESG integration, minimize greenwashing and deliver on the ESG promise.
The focus will also need to be on using artificial intelligence in areas such as rapid data aggregation, data quality assurance, analytics and smart reporting to achieve effectiveness and efficiency. In addition, an AI application with data aggregation based on environmental sensors could provide a critical data aggregation mechanism, which could then be integrated with the business side 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 from the bottom up to drive real results. And with this, times may actually be changing for the better for ESG.