Artificial Intelligence (AI) in Sustainable Finance and Green Banking

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

🔬研究者:Provides a conceptual model linking AI techniques to sustainable finance outcomes, offering a foundation for empirical studies.

🏢実務担当者:Demonstrates how AI can enhance ESG analytics, green loan allocation, and climate risk management for financial institutions.

🏛政策担当者:Emphasizes the need for ethical AI governance, harmonized ESG taxonomies, and regulatory coordination in sustainable finance.

The global financial system is experiencing a structural transformation driven by climate change risks, environmental degradation, and heightened demands for corporate accountability. Sustainable finance and green banking have evolved from voluntary ethical initiatives into regulatory and systemic necessities aimed at aligning capital flows with long-term environmental and social objectives. However, traditional financial risk assessment models remain ill-equipped to integrate fragmented Environmental, Social, and Governance (ESG) disclosures, forward-looking climate uncertainties, and sustainability-linked systemic risks. Artificial Intelligence (AI) has emerged as a transformative technological catalyst capable of redefining sustainable financial intermediation through advanced data analytics, predictive modeling, natural language processing, and automated decision-making. This study critically examines the structural role of AI in enhancing ESG integration, climate risk analytics, green credit allocation, and sustainable portfolio management. Using a descriptive-analytical research design grounded in secondary data, institutional sustainability reports, regulatory frameworks, and peer-reviewed literature, the paper develops an integrated conceptual model linking AI capabilities to sustainable finance outcomes. The findings reveal that AI improves predictive precision, strengthens climate stress testing, reduces information asymmetry, enhances transparency in ESG scoring, and increases operational efficiency in green banking systems. Nevertheless, significant governance challenges remain, including algorithmic bias, model opacity, data quality limitations, cybersecurity risks, and regulatory fragmentation. The study concludes that AI-enabled sustainable finance represents not merely technological enhancement but a systemic reconfiguration of financial intermediation aligned with climate transition goals and long-term economic resilience. Policy implications emphasize ethical AI governance, harmonized ESG taxonomies, regulatory coordination, and institutional capacity building to ensure responsible, transparent, and inclusive deployment of AI technologies in sustainable finance.

• openalex https://doi.org/10.62656/sijss.v24i3.2420first seen 2026-07-25 04:58:43