AI-Powered Sustainable Finance: An Integrative Taxonomy and Framework of AI Applications for Sustainable Investment Decision-Making

2026-05-25

AI Summary

This study addresses the challenge of effectively analyzing and integrating ESG data in sustainable finance by proposing the first comprehensive artificial intelligence taxonomy and technical framework tailored for sustainable investment decision-making. The framework systematically integrates supervised learning, unsupervised learning, reinforcement learning, natural language processing, and optimization algorithms to support critical tasks such as ESG rating prediction, controversy detection, portfolio optimization, and sustainability report analysis. By synthesizing existing research and clarifying the roles of diverse AI methodologies in enhancing the efficiency of ESG data processing and the quality of investment decisions, this work offers a structured and scalable technical pathway to overcome prevailing ESG data barriers.

Abstract

The integration of Artificial Intelligence into sustainable finance represents a transformative paradigm shift in how Environmental, Social, and Governance factors are analyzed, predicted, and incorporated into investment decisions. This review provides a comprehensive taxonomy of AI approaches applicable to sustainable investment decision-making, categorizing methodologies based on their underlying algorithms and their impact on ESG-related financial processes. The proposed AI Taxonomy includes machine learning paradigms -- including supervised, unsupervised, and reinforcement learning -- as well as natural language processing techniques and optimization algorithms, examining their specific applications in ESG score prediction, controversy detection, portfolio management, and sustainability report analysis. By synthesizing findings from the recent literature, a framework emerges on AI-powered sustainable finance that identifies technological applications to overcome ESG data barriers.

Problem

• sustainable finance
• ESG factors
• investment decision-making
• AI applications
• ESG data barriers

Innovation

• AI taxonomy
• sustainable finance
• ESG data integration
• natural language processing
• machine learning paradigms