Sustainability Meets Intelligence: A PRISMA-Guided Systematic Review of AI’s Role in ESG Financial Decision-Making

Résumé

This study examines the transformative role of Artificial Intelligence (AI) in promoting sustainable financial decision-making within corporate environments. As firms face growing pressure to align financial performance with Environmental, Social, and Governance (ESG) standards, AI technologies, such as machine learning, natural language processing, and intelligent decision-support systems, are emerging as strategic tools to enhance transparency, mitigate risks, and foster long-term value creation. Following the PRISMA protocol, this research conducts a systematic literature review of 83 peer-reviewed studies published between 2015 and 2024 and indexed in Scopus. The review spans empirical, conceptual, and theoretical contributions across academic journals, conference proceedings, and industry reports. Through thematic analysis, four primary domains of AI application in sustainable finance are identified: ESG reporting, sustainability risk management, resource optimization, and strategic foresight. Findings indicate that AI improves firms’ ability to process complex ESG data, automate disclosures, forecast environmental risks, and optimize operations. Nonetheless, significant challenges remain, including fragmented data sources, algorithmic opacity, ethical dilemmas, and a lack of consistent regulatory frameworks. Moreover, research remains heavily concentrated in developed economies, with limited exploration of emerging regions like the MENA, despite their unique sustainability challenges. This paper offers a theory informed synthesis by integrating stakeholder theory, the resource-based view (RBV), and institutional theory, while emphasizing ethical governance of AI. It provides actionable insights for firms, especially SMEs, and policymakers seeking to promote responsible AI deployment. Ultimately, the study contributes to building a more resilient, inclusive, and sustainable financial ecosystem through digitally empowered decision-making.  

Classification JEL: B41, G32, Q56, O33  

Paper type: Theoretical Research