Green Intelligence in Finance: Artificial Intelligence-Driven ESG Analytics and Sustainable Investment Performance

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Empirical evidence on AI-enhanced ESG scoring methods and their impact on portfolio performance.

🏢実務担当者:Demonstrates how AI can extract deeper sustainability signals to build resilient portfolios.

🏛政策担当者:Highlights the need for ethical AI governance in sustainable finance to ensure transparency.

This study examined the role of artificial intelligence (AI)-driven Environmental, Social and Governance (ESG) analytics in enhancing sustainable investment performance. While traditional ESG ratings had been widely used in responsible investment strategies, they often suffered from data inconsistency, subjectivity and limited coverage of unstructured sustainability information. AI-based ESG systems were increasingly applied to extract deeper sustainability signals from corporate disclosures, reports and external data sources. Using portfolio-level analysis, this study compared the financial outcomes of portfolios constructed using AI-driven ESG indicators with those based on conventional ESG ratings. The results showed that AI-enhanced high-ESG portfolios achieved higher mean returns and superior Sharpe ratios than both AI-based low-ESG portfolios and traditionally rated ESG portfolios. In addition, AI-driven high-ESG portfolios demonstrated lower downside-risk exposure and smaller maximum drawdowns during market stress, indicating stronger resilience. Regression analysis further revealed that AI-derived ESG scores were more strongly associated with excess returns than traditional ESG metrics. These findings suggested that AI improved the informational efficiency of ESG assessment by capturing more accurate, forward-looking sustainability risks and opportunities. The study concluded that AI-driven ESG analytics strengthened the financial relevance of sustainability integration and supported better-informed investment decision-making. The results carried important implications for investors, regulators and corporations seeking to align AI deployment with high-integrity sustainable finance practices, while also highlighting the need for ethical and transparent AI governance in financial markets. References Albuquerque, R., Koskinen, Y., Yang, S., & Zhang, C. (2020). 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