Artificial Intelligence in ESG Monitoring

Artificial intelligence (AI) has become the new buzzword for ESG Monitoring. The use of artificial intelligence in ESG Monitoring is gaining ground among rating agencies, Environmental protection agencies and investment houses especially among hedge funds.

The benefits are clear: Companies that rank highly in ESG reports and ESG data see better financial performance, cheaper access to capital, and improved risk management. AI will increase this competitive advantage for progressive IR professionals and investors.

But how does artificial intelligence work? Let’s look. Here are some of the benefits of using AI in ESG Monitoring and regulations.

ESG Data Model and Data Automation

Machine learning can help overcome data integration challenges by integrating data from different datasets. It can identify similar fields and reduce noise while retaining most of the information. It also has the advantage of being able to mine vast amounts of unstructured data on public companies. This data can be used to compose meaningful ESG profiles of companies. The application of artificial intelligence in ESG Monitoring is not limited to, financial data; it is also applicable to the analysis of social media, image data and pdf reports and so on. We first need to create an ESG data model to host such data. We have built and automated computer vision applications to automate OCR to populate the data model and use utility bills as a data source.

The price of data is likely to rise. Several smaller firms are being acquired by large global companies. S&P Global acquired SAM, Moody’s bought a stake in climate risk specialist Four Twenty Seven, and Morningstar completed the acquisition of Sustainalytics. This trend will continue to grow, and the price of data may soon surpass the cost of a company’s stock. With such a growing demand for ESG data, the price of real time monitoring of ESG parameters will likely rise.

NLP:

As the stakes of SDG-aligned investing rise, so does the need for better ESG disclosure. Artificial intelligence technologies, such as Natural language processing (NLP), can help solve this problem by extracting meaningful information from news content. With the rise of AI, this technology has come a long way, enabling it to extract information from speech and text faster than a human analyst. To see how NLP can benefit ESG disclosure, here’s a quick look.

Programs are trained to understand the tone of a CEO’s speech can identify topics related to ESG (environmental, social, and governance) risks. Then, using natural language processing, these programs can infer whether the company is committed to mitigating environmental risks. The technology has many applications in ESG monitoring, including identifying the most profitable investments, predicting market trends, and interpreting historical data.

NLP can also help financial services companies understand the competitive landscape. By using sentiment analysis to identify companies with negative ESG characteristics, firms can evaluate their competitors’ performance based on their environmental, social, and governance ratings. Low ESG scores are negatively correlated with volatility. Using NLP to analyze the sentiment of their competitors’ customers, they can measure how healthy and successful their organizations are relative to others in the market.

Internal Rating Models:

An ESG rating is an important factor in evaluating a company’s social and environmental performance. Many investors have a limited knowledge of how ESG ratings are calculated. An ESG rating has significant implications for creditworthiness, as it impacts borrowers’ cash flows and default risk. High ESG performance should improve a company’s credit risk. However, high-level performance can bind scarce resources and result in conflicts of interest with agency partners. As such, it is important to avoid overinvesting in ESG to distract from inaccuracies and inappropriate company behavior. Instead, invest in ESG related initiatives that increase the asset’s value, and lower risk. AI driven internal rating models can help analyze the relationship between ESG ratings and other factors. In a recent research work for 100 large corporates in India, we could establish an ESG rating relationship with financial and non- financial information such as diversity indicators in the board.

Real Time Monitoring:

Real time ESG monitoring can act as an enabler for more sustainable business and help improve, the ESG rating for industries. According to World Economic Forum (WEF) analysis, 84% of it deployments are currently addressing, or have the potential to address, Sustainable Development Goals (SDGs). We can have a real time dashboard that can show SDG-related KPIs (water recycle rate, energy consumed, OEE,…) and sends alerts when a certain threshold is reached. In addition to allowing us to see the impact of investments, ESG real time monitoring can help improving the decision-making process for the long term. We have built models that run on streaming data and helps in finding industrial appliances have old engines and consumes more power than needed. Alert are triggered for an example if goes beyond acceptable limits.

Regulatory Monitoring:

Regulations governing the ESG field have increased rapidly in recent years, and companies are facing challenges in complying with them. For large companies with global operations, such changes can limit their ability to monitor their suppliers’ ESG performance. Artificial intelligence and machine learning (ML) tools can be used to detect and alert companies to regulatory changes in real time, thereby allowing them to adjust their ESG strategy to meet evolving regulatory requirements.

Forecasting and Optimization:

AI helps quantify carbon sources and emissions and can help companies and governments plan and act accordingly. We have been working with agencies in combining various data sources from macroeconomic and other sectors to see the impact on carbon forecast and build some short medium- and long-term forecasts. At the same time, we have been running optimization modules to suggest the effective utilization of energy sources. Machine learning and artificial intelligence can help companies reduce their carbon emissions by identifying the most efficient ways to use their resources.

The AI algorithms can be programmed to identify the best-suited areas for carbon-sequestration. These algorithms can analyze vast agricultural, meteorological, and geological databases. These models can perform large-scale carbon measurement in soil without the need for expensive human inputs. These new techniques are already making huge strides in the fight against climate change.

As artificial intelligence (AI) technology is increasingly used to improve ESG scoring systems we need to make sure they are reliable trustworthy and unbiased. To avoid the pitfalls of unethical AI, it is important to understand the potential for misuse. A good strategy for integrating AI into ESG Monitoring should be based on clear business and technical objectives.

If you have any interest on this topic or want to know more in detail, please reach out to us.

Happy learning,

DD