The Role of Artificial Intelligence in Advancing ESG Integration and Sustainable Finance: A Secondary Data Analysis
TL;DR
Findings indicate that AI technologies, particularly Natural Language Processing (NLP) and Machine Learning (ML), improve ESG data coverage by up to 40% and enhance predictive accuracy for climate-related financial risks, however, challenges related to data bias, model transparency, and regulatory fragmentation persist.
Abstract
The rapid growth of Environmental, Social, and Governance (ESG) investing has exposed limitations in traditional data collection, scoring, and risk assessment methods. This study examines how Artificial Intelligence (AI) is transforming ESG and sustainable finance using secondary data from institutional reports, academic literature, and market databases from 2020- 2025. Through systematic review and thematic analysis of secondary sources including Bloomberg ESG data, MSCI ESG Ratings methodology, World Bank sustainable finance reports, and peer-reviewed articles, this paper identifies key AI applications in ESG data aggregation, greenwashing detection, climate risk modeling, and portfolio optimization. Findings indicate that AI technologies, particularly Natural Language Processing (NLP) and Machine Learning (ML), improve ESG data coverage by up to 40% and enhance predictive accuracy for climate-related financial risks. However, challenges related to data bias, model transparency, and regulatory fragmentation persist. The paper proposes a conceptual framework for responsible AI adoption in sustainable finance and outlines future research directions.