Artificial Intelligence in Sustainable Finance: Mapping ESG Integration and Risk Detection Frameworks
人工知能によるサステナブルファイナンス：ESG統合とリスク検出フレームワークのマッピング (AI 翻訳)
Boutahir Omar, Lahlou-Kassi Habiba
🤖 gxceed AI 要約
日本語
本レビューは、人工知能（AI）がESG評価、リスク検出、投資戦略にどう活用されているかを40の研究から分析。機械学習や自然言語処理による透明性向上と、アルゴリズムバイアスやデータ不均一性といった課題を指摘。責任あるAIと標準化の必要性を強調。
English
This systematic review analyzes 40 studies on AI in sustainable finance, identifying five research clusters: ESG integration, FinTech innovation, risk detection, theoretical foundations, and ethical frameworks. It highlights AI's potential to enhance transparency and decision-making while addressing challenges like algorithmic bias, data heterogeneity, and lack of standardized ESG metrics.
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や有報における非財務情報開示の高度化が進む中、本論文はAIによるESGデータ分析とリスク検出の知見を提供し、実務への応用可能性を示す。
In the global GX context
With global frameworks like ISSB, CSRD, and SEC climate rules demanding robust ESG data, this review maps AI tools for integration and risk detection, emphasizing the need for standardized metrics and ethical AI governance.
👥 読者別の含意
🔬研究者:Provides a structured overview of AI applications in sustainable finance, identifying key research clusters and gaps.
🏢実務担当者:Offers insights on AI tools for ESG risk assessment and reporting, with caveats on data quality and interpretability.
🏛政策担当者:Highlights regulatory needs for AI transparency and ESG data standardization in sustainable finance.
📄 Abstract（原文）
This systematic review explores how Artificial Intelligence (AI) is being incorporated into sustainable investment practices, with a particular focus on its influence on decision-making processes, risk assessment, and the enhancement of environmental, social, and governance (ESG) outcomes. Drawing on 40 peer-reviewed studies published between 2018 and 2025, the review synthesizes the evolution of AI applications in responsible finance and identifies five major research clusters: (1) AI-driven ESG performance and sustainable finance integration, (2) AI, FinTech, and blockchain innovation for green finance, (3) AI-enhanced risk detection and financial resilience, (4) systematic and theoretical foundations of AI in sustainable finance, and (5) ethical, governance, and responsible AI frameworks. Findings reveal that machine learning, natural language processing, and big data analytics are increasingly used to evaluate ESG indicators, optimize investment strategies, and improve transparency in sustainability reporting. However, several challenges persist, including algorithmic bias, data heterogeneity, limited model interpretability, and the absence of standardized ESG metrics. These limitations highlight the need for greater model transparency, ethical accountability, and interdisciplinary collaboration between data scientists, financial practitioners, and policymakers. The review is framed by theoretical perspectives, including the Resource-Based View and Responsible AI frameworks, to contextualize AI’s role in sustainable investment. Also, this review demonstrates that AI is not merely a technological tool but a transformative driver of sustainable investment practices. By fostering responsible innovation, improving data reliability, and supporting evidence-based decision-making, AI has the potential to build a more transparent, resilient, and inclusive financial ecosystem, accelerating the global transition toward sustainable development.
🔗 Provenance — このレコードを発見したソース
- openaire https://doi.org/10.14419/166t8v12first seen 2026-05-14 21:31:24
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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。