← 論文一覧に戻る

Digital Transformation of Sustainable Finance: A Review of FinTech Applications in ESG Scoring, Climate Risk, and Green Bonds

サステナブルファイナンスのデジタルトランスフォーメーション:ESGスコアリング、気候リスク、グリーンボンドにおけるFinTech応用のレビュー (AI 翻訳)

Fahad Ullah Khan, Asad Amin

International Journal of Innovative Research in Computer Science & Technology📚 査読済 / ジャーナル2026-01-01#AI×ESGOrigin: Global経営インパクト: 資金調達対象セクター: finance
DOI: 10.55524/ijircst.2026.14.1.18
原典: https://ijircst.org/DOC/1446_pdf.pdf
📄 PDF

🤖 gxceed AI 要約

日本語

本レビューは、ESGスコアリング、気候リスク評価、グリーンボンドにおけるAI・機械学習・ビッグデータ・ブロックチェーンなどのFinTech応用を包括的に整理。従来の課題(データ品質・透明性不足)に対するデジタル技術の貢献と、バイアスやグリーンウォッシュ等の新たな課題を指摘し、信頼性向上に向けた研究方向を示す。

English

This review comprehensively surveys FinTech applications (AI, ML, big data, blockchain) in ESG scoring, climate risk assessment, and green bonds. It highlights how digital tools improve efficiency and data coverage while addressing challenges like biased algorithms, transparency, and greenwashing, suggesting future research directions for trustworthy 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

日本ではSSBJ基準や有報でのESG情報開示が進む中、FinTechによるデータ品質向上やグリーンウォッシュ防止は実務上の重要課題。本レビューは、AI活用による開示の効率化と信頼性向上への可能性とリスクを提示し、日本の開示インフラ整備や投資家対応に示唆を与える。

In the global GX context

As TCFD/ISSB/CSRD mandates expand globally, this review provides a timely overview of how AI and other FinTech can enhance ESG data reliability and scalability. It identifies critical challenges (algorithmic bias, unclear models) that regulators and standard-setters must address to ensure digital tools support rather than undermine sustainable finance credibility.

👥 読者別の含意

🔬研究者:Provides a structured map of current FinTech applications in sustainable finance, highlighting under-explored areas and methodological gaps for future research.

🏢実務担当者:Offers a concise overview of digital tools available for ESG scoring, climate risk, and green bond platforms, with warnings on bias and transparency issues to consider in adoption.

🏛政策担当者:Identifies regulatory gaps around AI-driven ESG scoring and greenwashing risks, informing potential policy interventions to foster trustworthy digital sustainable finance.

📄 Abstract(原文)

Sustainable finance is booming globally. Investors, governments and institutions are now demanding environmentally responsible, socially responsible, and good governance financial systems. These goals are generally quantified using environmental, social and governance (ESG) indicators. However, traditional finance systems can have issues like poor quality of data, lack of transparency and slow reporting and lack of trust in ESG scores. These issues are causing it to be difficult to measure sustainability in a reliable way. Financial technology (FinTech) is revolutionizing sustainable finance. New digital tools like artificial intelligence (AI), machine learning, big data and blockchain are now being used to collect, process and analyse ESG and climate related information. These tools are used to help investors and policymakers to make better and faster decisions. This review paper provides an overview of recent research on how FinTech can contribute to sustainable finance. It is focused on three main areas; ESG scoring and measurement AI-based climate risk assessment and digital platforms for green bonds and other sustainable debt instruments. The study is based on integrative approach of narrative review with a clear and transparent search process of the literature. Academic articles and important institutional reports that were published in the recent years are reviewed. The findings reveal that FinTech contributes to more efficiency, better data coverage and accessibility to sustainability information. At the same time, important challenges remain such as biased algorithms, unclear AI models, weak regulation and the risk of greenwashing. This review highlights these issues and suggests directions for future research to improve trust, transparency and ethical use of digital technologies in sustainable finance.

🔗 Provenance — このレコードを発見したソース

🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録(無料・3キーワードまで)。

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。