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<p>Artificial Intelligence (AI) is increasingly pivotal in sustainable green finance, supporting risk analytics, investment management, environmental, social, and governance (ESG) assessment, and sustainable reporting. This review synthesizes recent literature on how machine learning, deep learning, and natural language processing enhance credit-risk modeling, portfolio optimization, and ESG evaluation. Key developments include default prediction using novel variables (e.g. household or climate data), deep learning for market forecasting and portfolio returns, and natural language processing to detect greenwashing and improve ESG ratings. AI tools also enable personalized green investment strategies and more automated reporting, but they raise challenges in ethical AI governance, algorithmic bias, regulatory compliance, and AI’s own carbon footprint. Two summary tables distill applications in (1) risk and investment management and (2) ESG and sustainability reporting. We conclude with a focused interdisciplinary research agenda for responsible AI in green finance.</p>

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Artificial intelligence applications for advancing sustainable green finance

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月26日

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  • Nuraini Desty Nurmasari,
  • Muhammad Roil Bilad

摘要

Artificial Intelligence (AI) is increasingly pivotal in sustainable green finance, supporting risk analytics, investment management, environmental, social, and governance (ESG) assessment, and sustainable reporting. This review synthesizes recent literature on how machine learning, deep learning, and natural language processing enhance credit-risk modeling, portfolio optimization, and ESG evaluation. Key developments include default prediction using novel variables (e.g. household or climate data), deep learning for market forecasting and portfolio returns, and natural language processing to detect greenwashing and improve ESG ratings. AI tools also enable personalized green investment strategies and more automated reporting, but they raise challenges in ethical AI governance, algorithmic bias, regulatory compliance, and AI’s own carbon footprint. Two summary tables distill applications in (1) risk and investment management and (2) ESG and sustainability reporting. We conclude with a focused interdisciplinary research agenda for responsible AI in green finance.