ESG needs a rethink: technology is key

Regulations will change the ESG landscape

In recent months, a range of regulatory interventions have emerged – from the Sustainable Finance Disclosure Regulation (SFDR) in Europe to the Securities and Exchange Commission’s own Task Force on Climate-Related Financial Disclosures (TCFD) in the US. The elephant in the room, however, is this: how do we really tap into 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 artificial intelligence can ultimately help.

Top issues haunting ESG ratings and the investment world:

  1. Regulation: ESG regulations are under development. And fast. The challenge of competing regulations, jurisdictions, interoperability, alignment and more makes adoption late at best when it comes to assimilating policies and regulations in global markets. While companies are still struggling to integrate ESG in a meaningful way, they are already under pressure to report on TCFD and soon TNFD, the Taskforce on Nature-Related Financial Disclosures.
  2. Data Assurance: Data assurance in ESG integration needs to be addressed urgently and comprehensively. The breadth, availability and veracity of the underlying ESG data is essential to overcome the problem of greenwashing and the resulting risk to investors. We need to monitor and properly validate ESG data, working with its subjectivity rather than overlooking its subjective nature, but to deliver meaningful results, regarding ESG
  3. Craftsmanship and capacity: There is currently a significant talent gap in the ESG market, which will only widen as various stakeholders, from corporations and consultancies to ESG rating companies and financial institutions, fight for the limited talent. The talent gap ultimately affects the application of ESG and the quality of the underlying data that is collected and delivered to or by companies. The supply of talent will only grow as academic institutions begin to scale their offerings in this space.
  4. ESG integration: Without the required talent depth and tools, ESG integration in many companies remains largely superficial. This is in no way helpful, as it only increases doubts about the usefulness of ESG. More than anyone else, companies need to recognize the usefulness of ESG adoption if it is to really materialize the way it should be.

Simply put, the accuracy of ESG analysis is far from what is commonly observed in areas 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 a cross-section of global companies, I believe that while ESG is still used by many investment professionals as a tool for marketing, it doesn’t always lead to true integration. and not getting a handle on the decision units in the c-suite and boardrooms. ESG clearly needs a rethink.

Key to enabling this change is the selective integration of AI technology into current ESG processes and methodologies. Major ESG players have been experimenting with using AI to improve their ESG ratings. But the fuzzy nature of ESG aspects still makes this very difficult, if not impossible, through traditional machine learning (ML) approaches. The problems outlined above only add to the challenge.

Where’s a good place to start with ESG?

A good starting point would be a calibrated approach with 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’ needed to fundamentally improve ESG integration, minimize greenwashing and deliver on the promise of ESG.

The focus will also need to be on using AI 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 environmental sensor-based data aggregation could provide a critical data aggregation mechanism, which could then be integrated with the business end of the spectrum.

Leveraging AI through a combination of these steps will drive deep and meaningful business integration and related analytics based on the bottom-up designed models to deliver real results. And with this, times could even change for the better for ESG.

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