By: Guhan, Senior Consultant

Capital markets are in dire need to adapt to the ever-evolving changes in Environmental, Social, and Corporate Governance (ESG) ratings. They cannot disregard the significance of ESG factors while making important investment decisions. Investors and corporate companies appreciate the potential effect that ESG has on the performance of financial securities.

ESG rules help financial investors analyze the unmeasured or unprecedented changes in ESG before making investment choices. It helps provide data that companies usually do not get access to or consider while making crucial investment decisions and determining the sustainability of the business in the longer run.

What is an ESG rating?

ESG are the components that help financial investors determine the perseverance and moral effect of an investment while conducting a business. Socially accountable investors validate ESG rating at the time of investment. ESG stands for:

• Environmental: efforts by organizations towards protecting the earth and our environment.
• Social: a view of the perception of the organization in the minds of the public, including factors such as labor operations, safety principles, equal opportunities for all, quality of products/services offered, etc.
• Governance: corporate governance standards are followed by the leadership team. For example, employee compensation, ethical business practices, board diversity, and overall transparency.

How does ESG rating work?

Investors need an approach to assess the ESG performance of an organization impartially. This led to the growth of various ESG rating agencies such as MSCI, FTSE, and ESG, which assess organizations worldwide on their ESG performance and make this information accessible to their customers. These rating agencies help investors in understanding the ESG risk associated with their business. Along with an overall score, companies are evaluated individually for Environment, Social, and Corporate Governance. Companies with better ESG ratings have a better understanding of future risks and opportunities in their area of operations.

The challenges in ESG reporting

However, the crucial challenge in ESG reporting is the unavailability of standard normalized ESG guidelines. The data analysis and interpretation of ESG vary across various investing networks and jurisdictions. Investors are not able to access the relevant information across the different corporations and national boundaries. The top challenges are:

1. Sort of data: corporations must collect ESG data from different internal and external sources in several formats and schemas.
2. Rapidity: companies need to analyze the data quickly as ESG data tends to change frequently. The quicker the data is collected, the quicker the company can correct it.
3. Volume: companies need to analyze a large volume of data to ensure that there is no deterioration in their ESG performance.
4. Lack of standardized ESG metrics: it is difficult to use a consistent rating structure across different investments because there aren’t any standardized ESG metrics.

If companies do not address the above problems, it won’t be easy to analyze or accurately understand their ESG data. That brings us to the question, can artificial Intelligence help solve major challenges and complexities involved in ESG rating and provide valid data that can be accessed by investors/corporates? Can artificial intelligence solve problems pertaining to infrequent data and delays in capturing data?

How can AI transform the ESG rating process?

Without any doubt, only with the help of AI predictive analysis can companies enhance their operational performance. Once companies have their own ESG performance controlled, they can focus on how they are identified by consumers, investors, and the market.
According to the Greenwich associates survey (2018), only 17% of the investment profiles currently use AI as part of their process and 40% of the respondents stated they would increase the budget for AI in the future.

How can AI help?

To examine unstructured data and make important decisions, companies need to embrace tactical and calculated techniques. A significant part of artificial intelligence in ESG comes from sentiment analysis algorithms. These calculations permit personal computers to examine the tone of the discussions and a job code that couldn’t perform successfully. However, with the use of AI technologies and Natural Language Processing (NLP) techniques, companies can analyze a huge amount of data seamlessly. Also, the sentimental analysis might be layered onto text-based evaluations to survey an organization’s view of patterns and advancements.

• Humans manually gathering information from social media, daily local news, and freshly available reports is a huge task that is error-prone, time-consuming, and costly. Artificial intelligence solution helps investment researchers identify ESG risk by drilling down vast amounts of qualitative, unstructured data through AI algorithms that quickly identify, extract, and measure ESG information.
• AI algorithms that support real-time ESG analysis will signal early warnings and timely indicators and accurately identify trends in a company’s activities. This captures consumers’ sentiments to gauge the impact of ESG and gives clear pictures to the investors to evaluate companies’ performance and future risks in their investment.
• ESG research will be easier by developing AI models to create a standardized framework and present the findings in custom reports and visualizations. Investment bankers and portfolio managers use ESG ratings and reports for portfolio construction, risk management, thought leadership, and benchmarking/index-based products to make sustainable investments.

Challenges in adopting AI

The problem with AI and machine learning is that they depend mainly on Natural Language Processing to determine whether the quality of the data is valuable.

Large volumes of data in AI applications could lead to a storage problem for businesses. When we adopt data-driven automation techniques, companies may be required to handle personal or sensitive data, leading to data security-related issues.

IT engineering must be redesigned to accommodate artificial intelligence. Moving from old frameworks to advanced frameworks would be difficult for Fund managers. They need to constantly update themselves regarding the recent changes in techniques in ESG rating and choose the appropriate technique that suits their line of business.

Conclusion

Maintaining ESG standards and practices is essential for the long-term survival of businesses. A growing number of investors invest their money to make a positive impact on society. Investment managers are experiencing extraordinary stress to quantify ESG models in their portfolios. The absence of information is making it difficult for them to evaluate long-term risk. Investors will be aware of the risk and long-term sustainability of the company using ESG ratings.

Artificial Intelligence is the problem solver. AI applications enable them to search and process large volumes of data, capture the ESG information quickly, and provide the findings in a visual format. AI-driven ESG models give investors a standardized framework for assessing the company’s ESG impact, predicting trends, and future risks that can impact their investments. Eventually, investors will get ESG data quicker with more quality to make socially responsible, environment-friendly investments.

References:

1. Better ESG Reporting—A Key to Strengthening Capital Markets (ifc.org)
2. The need for speed: sentiment analysis in ESG measurement | Alva (alva-group.com)
3. How can AI help ESG investing? | S&P Global (spglobal.com
4. http://web.archive.org/web/20210803063401/
5. https://nexusfrontier.tech/blog/ai_for_good_the_case_of_using_the_technology_for_esg
6. https://www.theimpactivate.com/artificial-intelligence-opens-new-frontiers-in-esg-data
7. https://www.morganstanley.com/ideas/corporate-esg-capital-markets