Developing ESG Through AI


The best practices for setting objectives, evaluating results, and reporting ESG progress are made simple by combining existing organisational data with AI.

In 2022, government leaders and corporate boards will “face rising pressure to demonstrate” that they are adequately equipped to oversee and understand ESG issues — from climate change to human rights to social unrest, according to S&P Global.

Today, environment, social, and governance (ESG) reporting has entered the meeting boardroom. However, it has never been simple, and firms are striving to satisfy the demands of all stakeholders as pressure intensifies. The drive to achieve net-zero carbon emissions globally by 2050 as per The Paris Agreement tied with the pandemic has made ESG criteria go from mainstream to mandatory.

Tesla, the electric-vehicle maker, was not given a place on the S&P 500′s ESG Index, in response to which Elon Musk called ESG a scam.

“Exxon is rated in the top ten best in the world for the environment, social & governance (ESG) by S&P 500, while Tesla didn’t make the list! ESG is a scam. It has been weaponised by phony social justice warriors,” tweeted Musk.

Musk could have reached out to AI. The adoption of AI for making decisions related to ESG investing helps mitigate the risks concerned with the long-term sustainability of any business. However, certain challenges exist that first need to be taken care of.

Challenges at hand

Since concepts like sustainability and diversity can imply various things to different businesses, the uneven nature of ESG frameworks across firms presents a severe problem for its reporting. In addition, comparing progress and establishing industry-wide standards is made more difficult by the wide range of frameworks within each industry.

Besides inconsistent frameworks, inefficient data collection methods frequently obstruct ESG reporting. For instance, despite working diligently to achieve its sustainability objectives, inaccurate data classification may result in underreporting a power plant’s efforts. Last but not least, it might be difficult to distinguish what an organisation achieves and strives to do.

Today, the new-age tech, particularly AI, has answers for the challenges businesses need to address.

AI to rescue

It is essential to note that AI can turn data into structured insights that give business executives and organisations a better understanding of their environment and faster identification of dangers, risks, and opportunities. In addition, this gives them a more outside-in viewpoint on topics like ESG. AI can help do the following:

Analyse data: AI enhances data gathering techniques by assisting utilities in keeping a record of all ESG actions. The best approach for ensuring that the organisation can comply with its own standards and other legal obligations when investors and other organisations investigate is to keep a ledger of all papers relevant to ESG initiatives. However, without technology, it would be challenging for any firm to go through hundreds of pages of documents and only save those pertaining to ESG reporting.

To provide a variety of potential prediction indications, NLP algorithms scan articles, categorise items, and extract positive and negative sentiments. They allow investors to explore a wide range of underlying data categories, or they can roll up all the ESG scores at the portfolio level to see how exposed they are to various ESG issues. When it’s time to report results, AI can skim through documents, analyse key terms, and save data from pertinent files.

Make better predictions: Leveraging simpler pattern recognition techniques, like kernel classifiers or logistic regression models, helps build better predictive abilities. The AI system can gather data and historical knowledge to make recommendations regarding setting a goal for the company, which is specific, measurable, achievable and time relevant.

Further, the AI solution can continue to learn as experts adopt — or reject — these recommendations, reducing the cognitive load on experts for each decision while also assisting in preventing drift and bias in the judgments themselves.

Helps adapt to growing trends: Deep learning is conducive to processing complicated, rich, and multidimensional data. Every organisation receives colossal amounts of data in a variety of formats. As a result, it is simpler for businesses to apply deep learning to data sets comprising speech, photos, PDFs, and videos to use unstructured data and adapt to market trends and needs.

AI and machine learning can alter the future of ESG reporting by automating time-consuming tasks and assisting companies in remaining compliant with stakeholders without incurring high costs.

Summing up

It is believed that ESG integration and transparency will grow in importance and that this development will be aided by the ability to work with reliable data. AI can be the fundamental component in assisting risk managers and investors in analysing ESG data that may be gathered in both structured and unstructured formats. AI provides exciting prospects to build new data sources and assists in extracting pertinent information from already existing ones.

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