Having a solid understanding of ESG information through data mining is critical for data analytics and consulting, just like a strong foundation is essential for building a house. Without it, both can crumble under the weight of uncertainty, while a robust base ensures strength and growth. The remarkable advances of generative Artificial Intelligence (AI) have provided powerful tools to grow and strengthen ESG data production and assessment. Innovations that integrate alternative data sources enhance and complement core financial reports by providing additional ESG insights. By reducing the subjectivity and cognitive bias that can accompany human-led analysis, AI can help investors and asset managers make better investment decisions. Moreover, ESG ratings and analytics providers can create more objective ratings by taking an outside-in perspective of companies’ sustainability performance with the help of AI.¹

Today, AI is utilized in various ways for ESG research. AI-powered technologies such as Natural Language Processing (NLP), Computer Vision, Machine Learning (ML), and Deep Learning are utilized by data aggregators and ESG analytics providers to gather and scrutinize data from numerous sources. NLP plays a crucial role in synthesizing vast amounts of unstructured data from the internet and online media. It utilizes automatic summarization, relationship extraction, and sentiment analysis to accurately determine the public’s sentiment toward a company. This approach effectively gauges the external perception of a company, offering valuable insights into its reputation.

Sustainable investment is also benefiting from the application of ML thanks to cognitive computing processes, as AI can assign quantitative values to large volumes of qualitative ESG data. As a result, unstructured datasets are becoming more structured and easier to analyze. Utilizing AI algorithms can also help fill in missing data points, such as greenhouse gas emissions and energy consumption, to more accurately assess a company’s ESG performance based on its disclosed information. Real-time monitoring through satellite remote sensing images can enhance the credibility of self-disclosure and reveal any potential risks of “greenwashing.” Additionally, asset managers in Europe are utilizing AI to effectively address challenges posed by the SFDR and EU taxonomy, by developing AI models that apply the taxonomy criteria to investment strategies. This enables them to offer green investment options more confidently to investors.²

Since 2010, Evalueserve has effectively implemented its Mind+Machine approach to solve clients’ challenges by capitalizing on the powerful combination of analytics and AI. As an integral player in the ESG data value chain, Evalueserve has created its own AI-powered tools to speed up the production of fundamental ESG data collection and assessment. The benefits of this combination of AI and ESG are numerous and significant. With our extensive knowledge in the realms of ESG data and AI, we possess a profound understanding of the substantial market benefits arising from their convergence.

The Advantages of Intelligent Analytics.

AI has been incorporated into ESG performance evaluation to enhance analyst efficiency and quality. Two of the more prominent examples of these advantages include smart data collection and smart analysis and alternative scoring.

Smart Data collection. When evaluating companies in emerging markets, where disclosure is not mandatory for many industries, analysts may face limitations in obtaining data from well-formatted reports. However, AI can offer a solution through smart data aggregation. By utilizing NLP techniques, Optical Character Recognition (OCR), and Auto Speech Recognition (ASR), analysts can readily extract and integrate ESG data from various unstructured sources, such as PDF reports, webpages, news articles, pictures, and video messages.

These technologies increase the efficiency of sustainability performance and controversies assessment. For instance, when it comes to Chinese companies, self-disclosure for A-share companies is unstructured under the “Governance” category, as they publish different announcements annually regarding the board and the board of supervisors alongside annual reports. Meanwhile, information under the “Social” and/or “Environmental” categories is often much more limited. AI can help analysts save time and effort, thereby reducing costs for clients, by collecting unstructured data and providing alternative sources that supplement self-disclosure to create a comprehensive assessment method that is more meaningful to the market.

Smart Analysis & Alternative Score. Utilizing deep learning in its ESG assessment methodology, an AI model can generate an impartial ESG score, effectively tackling the problem of subjectivity when evaluating qualitative ESG metrics, which often comprise the bulk of an ESG evaluation. Increasingly, ESG rating providers are generating ESG scores by collecting unstructured data from internet sources, not just company disclosures, largely replacing human research analysis with algorithms and systematically applying standardized frameworks such as SASB.³

An advantage of AI models is increased transparency, offering insights into the applied methodology. A comparison study of AI-driven and analyst-driven scores shows that AI provides ratings with a more democratic aggregation process and contributes to real-time compression of controversies.⁴ AI-driven research is collecting data from the internet, then proceeding with analytics and providing ratings per the set algorithms. Meanwhile, analyst-driven research uses AI to accelerate data collection but still conducts manual analytics (see Figure 1). Further, AI can provide a more objective evaluation of companies’ ESG performance, unlike other providers that use a subjective feedback cycle. However, collecting data widely from the internet poses information security risks and heavily relies on a robust framework from authorities. So, too, its feasibility is limited to developed markets and may lack credibility in cases of emerging markets where the disclosure guidance is still progressing. This highlights a key point that despite AI’s advances, human expertise remains a necessary element for completing a well-versed evaluation.