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AI-Powered ESG Reporting Assistance

ESG compliance necessitates timely processing of large quantities of unstructured data, a task that AI is particularly strong at. With AI-Powered ESG Reporting Assistance, investors can speed up ESG reporting, contain costs, and reduce compliance risks.
Published by Jiulin Teng on 21 Nov 2021 · Keywords: aiesgregulationsfdrvogosen
With the growing importance of ESG, most investors—especially small investment firms—must do more in less time. Information systems and tools that enable insights into ESG processes and allow smart automation of repeatable yet evolving tasks, are gaining momentum.
Instead of wasting man-hours, AI-powered ESG Reporting Assistance automatically and efficiently takes care of tedious jobs with a level of consistency that human analysts cannot match. Tasks such as reading through reams of ESG documents and questionnaires, drafting responses, clustering relevant pieces of information together, and scouring petabytes of ESG data to find answers are where AI excels.
AI, and specifically Natural Language Processing (NLP) technology, has an exceptional ability to extract information from unstructured data and can be trained to recognize ESG concepts and/or classify information with high degree of precision. It can form the backbone of a highly automated system that speeds up ESG reporting, contains costs, and reduces compliance risks.
Moreover, the entire history of ESG reporting data can be tracked and analyzed. From these, ESG Reporting Assistance AI can provide crucial insights for future reporting endeavors, allowing reporting teams to work in a fast, smart, and evidence-based environment with advanced analytics support.
To see how an ESG Reporting Assistance AI would help you with SFDR compliance, check out our White Paper on AI in ESG.
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Jiulin Teng

Jiulin Teng has deep understanding of key topics in management science and new technologies. He also has broad knowledge in macroeconomics, business policy, and software engineering.
Upon completing his dissertation on the application of game theory in competitive strategy, Jiulin Teng worked as a consultant at McKinsey & Company (Belgium). More recently, he has launched and participated in a number of startups in blockchain, IT, and AI. He is a proficient database architect and is a full-stack developer with good command of modern programming languages. He has also taken an interest in Natural Language Processing.
Jiulin Teng holds a PhD in Strategic Management and Business Policy from HEC Paris (France). He also has a Master’s degree in Bioentrepreneurship from Karolinska Institute (Sweden) and a Bachelor’s degree in Biological Sciences from Nankai University (China).