Role of Artificial Intelligence in Promoting Green Lending and Sustainable Banking: A Conceptual Study
グリーン融資と持続可能な銀行業務の促進における人工知能の役割：概念的研究 (AI 翻訳)
Poojary, Jyothi, Pathan, Afsha, Duste, Thaireem
🤖 gxceed AI 要約
日本語
本概念研究は、AI（機械学習、予測分析、NLP）が銀行のESGリスク評価、グリーン投資機会の特定、持続可能性フレームワークへの準拠をどのように支援できるかを検討する。AIを活用した信用スコアリングは環境リスクを組み込み、責任ある融資を可能にする。データ可用性や倫理的課題も指摘し、持続可能な金融への概念的基盤を提供する。
English
This conceptual study explores how AI (ML, predictive analytics, NLP) can support banks in ESG risk assessment, identifying green investment opportunities, and ensuring compliance with sustainability frameworks. AI-enhanced credit scoring incorporates environmental risk factors, enabling responsible lending. It also highlights challenges like data availability and ethics, providing a conceptual foundation for sustainable finance.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本の金融機関はSSBJ開示やグリーン投資家対応が急務であり、AIを活用したESG評価は融資判断やポートフォリオ管理に有用。本稿は概念整理を提供し、今後の実証研究や政策検討の土台となる。
In the global GX context
Globally, AI-driven ESG assessment is becoming critical for banks under TCFD/ISSB frameworks and transition finance. This paper offers a conceptual framework for integrating AI into green lending, relevant for regulators and financial institutions aligning with CSRD and SEC climate rules.
👥 読者別の含意
🔬研究者:AI×ESG金融の概念整理と将来の実証研究の方向性を示す。
🏢実務担当者:銀行の融資プロセスにAIを組み込む際の論点と可能性を理解できる。
🏛政策担当者:AIを活用したグリーン金融促進政策の検討に示唆を与える。
📄 Abstract（原文）
Abstract The growing urgency of climate change and environmental degradation has compelled the financial sector to adopt sustainable practices, particularly through green lending and sustainable banking. Artificial Intelligence (AI) has emerged as a transformative enabler in this transition by enhancing decision-making, risk assessment, and operational efficiency. This conceptual study explores the role of AI in promoting environmentally responsible financial systems. It examines how AI-driven tools—such as machine learning, predictive analytics, and natural language processing—can support banks in evaluating environmental, social, and governance (ESG) risks, identifying green investment opportunities, and ensuring compliance with sustainability frameworks. Additionally, AI facilitates improved credit scoring models that incorporate environmental risk factors, enabling more accurate and responsible lending decisions. The study also highlights challenges such as data availability, ethical concerns, and regulatory complexities. By integrating AI into their core strategies, financial institutions can accelerate the transition toward a low-carbon economy while maintaining profitability and resilience. This paper provides a conceptual foundation for future empirical research and policy development in sustainable finance. Keywords: Artificial Intelligence (AI); Green Lending; Sustainable Banking; ESG (Environmental, Social, and Governance); Machine Learning; Climate Risk Assessment; Sustainable Finance; Predictive Analytics; Financial Innovation; Environmental Sustainability.
🔗 Provenance — このレコードを発見したソース
- openaire https://doi.org/10.5281/zenodo.21155035first seen 2026-08-04 04:41:26
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gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。