Artificial intelligence in sustainable finance and Environmental, Social, and Governance (ESG) performance: A review
サステナブルファイナンスとESGパフォーマンスにおける人工知能：レビュー (AI 翻訳)
Vibhavari Prasad Mane
🤖 gxceed AI 要約
日本語
本レビューは、AI（機械学習、NLP、予測分析）がESG評価、ポートフォリオ管理、リスク分析、情報開示をどう改善するかを体系的に整理。同時に、データ品質、アルゴリズムバイアス、規制遵守といった課題を指摘し、信頼できるAIと持続可能なデジタル金融エコシステムの必要性を論じる。
English
This systematic review explores how AI techniques (machine learning, NLP, predictive analytics) enhance ESG measurement, portfolio management, risk analysis, and sustainability disclosure. It also highlights challenges like data quality, algorithmic bias, and regulatory compliance, calling for trustworthy AI and sustainable digital finance ecosystems.
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
日本ではSSBJ開示基準や統合報告へのAI活用が進むが、本レビューはESG評価の精度向上やグリーンウォッシュ検知など実務への示唆に富む。特に、AIによるリアルタイムリスク追跡は日本企業の投資家対応にも有用。
In the global GX context
As global ESG disclosure frameworks (ISSB, CSRD) demand more granular data, AI-driven analytics offer efficiency gains. This review maps the current landscape of AI in sustainable finance, providing a baseline for practitioners and policymakers to assess both opportunities and risks such as algorithmic bias and regulatory gaps.
👥 読者別の含意
🔬研究者:Provides a structured overview of AI applications in ESG, identifying research gaps and methodological issues for future studies.
🏢実務担当者:Highlights practical AI tools for ESG scoring, risk tracking, and disclosure automation, but warns of data quality and bias pitfalls.
🏛政策担当者:Underscores the need for governance frameworks around AI in sustainable finance, including ethical AI and regulatory compliance.
📄 Abstract（原文）
Artificial Intelligence (AI) is rapidly changing sustainable finance and Environmental, Social, and Governance (ESG) performance in particular, but the disjointed nature of current studies does not allow us to fully comprehend how machine learning, ESG analytics, responsible AI, and sustainable investing all contribute to financial decision-making and corporate sustainability performance. Although the context of AI-based ESG scoring, green finance, and data-driven sustainability reporting develops rapidly, the literature is spread out among the fields of finance, management, and technology and requires the application of the PRISMA framework to provide a clear picture of transparency and methodological rigor in systematic reviews. The present review examined the intersection of artificial intelligence, sustainable finance, ESG performance, financial technology (FinTech), climate risk analytics, algorithmic governance, and responsible investing. The paper provides an assessment of the effectiveness of artificial intelligence methods, including machine learning, natural language processing, predictive analytics, and modeling the sustainability of big data, in improving ESG measurement, managing portfolio, risk, and disclosure to sustainability. Specific focus is placed on such new themes as AI-enabled ESG ratings, green innovation, ethical AI, regulatory technology (RegTech), and explainable AI in finance that are becoming highly influential in the international financial markets. The results show that AI drastically enhances the ESG performance analysis, sustainable investment plan, and transparency of the companies and also facilitates the real-time tracking of the environmental and social risks. Nevertheless, the literature also singles out endemic data quality issues, algorithmic bias, governance frameworks, and regulatory compliance as recent concerns that require trusted AI and sustainable digital finance ecosystems.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.70593/deepsci.0202033first seen 2026-05-06 00:29:04
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。