IMPROVING THE ECONOMIC MECHANISMS OF DEVELOPING GREEN FINANCING SERVICES IN COMMERCIAL BANKS BASED ON ARTIFICIAL INTELLIGENCE TECHNOLOGIES: A FRAMEWORK FOR SUSTAINABLE BANKING TRANSFORMATION
Abstract
The accelerating climate crisis and the implementation of the United Nations Sustainable Development Goals (SDGs) have placed commercial banks at the centre of the global sustainability transition, intensifying the demand for technologically advanced green financing services. This study investigates the integration of artificial intelligence (AI) technologies into the economic mechanisms of green financing in commercial banks, with the objective of designing a comprehensive framework for sustainable banking transformation. Using a mixed-methods approach combining a systematic literature review (n = 142 Scopus-indexed publications, 2015–2024), comparative case-study analysis of 25 commercial banks across emerging and developed markets (2020–2024), and the development of a supervised machine-learning model for green credit scoring, the research evaluates how AI-driven mechanisms can enhance the efficiency, accuracy and scalability of green financing operations. The empirical analysis demonstrates that an AI-integrated green credit scoring model achieves an Area Under the Receiver Operating Characteristic Curve (AUC-ROC) of 0.891, representing a 23.4 % improvement over conventional credit-scoring systems. Banks deploying AI-enabled climate-risk assessment tools reported, on average, a 31 % reduction in default rates on green loan portfolios and a 17.8 % increase in green loan origination volumes.
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