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Does the use of ESG scoring in FinTech platforms enhance ethical financial decision-making, or does it create new forms of bias?

FinTechプラットフォームにおけるESGスコアリングの利用は倫理的な金融意思決定を促進するか、それとも新たなバイアスを生み出すか？ (AI 翻訳)

Rongchen Zou

Finance & Economics📚 査読済 / ジャーナル2026-08-13#AI×ESG経営インパクト: 資金調達対象セクター: finance
DOI: 10.61173/c7d12x14
原典: https://doi.org/10.61173/c7d12x14

🤖 gxceed AI 要約

日本語

本論文は、AI駆動の投資システムや信用評価アルゴリズムなどのFinTechツールに組み込まれたESG評価枠組みを検討する。ESG採用は透明性と長期的リスク監視を向上させる一方、評価機関間の不一致、データ品質の低さ、アルゴリズムバイアス、グリーンウォッシュのリスクが残る。実証分析では、ESGデータの乖離と不完全な開示が誤った投資判断や構造的不平等を強化する可能性を示し、新興市場で特に顕著である。標準化、データ信頼性、アルゴリズム規制の必要性を強調する。

English

This paper examines ESG rating frameworks embedded in FinTech tools such as AI-driven investment systems and credit-assessment algorithms. While ESG adoption improves transparency and long-term risk oversight, inconsistencies among rating providers, poor data quality, algorithmic bias, and greenwashing risks persist. Empirical evidence shows that ESG data divergence and incomplete disclosures can lead to misinformed decisions and reinforce structural disparities, especially in emerging markets. The paper calls for better standardization, data reliability, and clear algorithmic regulation to reduce bias and achieve ethical financial outcomes.

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開示基準の適用が進む中、ESGスコアリングの信頼性とアルゴリズムバイアスは投資家対応や開示実務に直結する。本論文は、日本のFinTech企業や金融機関がESGデータを活用する際のリスク認識を高め、規制対応の示唆を与える。

In the global GX context

Globally, the paper contributes to the discourse on ESG data quality and algorithmic fairness in financial technology, relevant to ISSB standards and emerging regulations on AI and sustainable finance. It highlights the need for standardized ESG metrics and transparent algorithms to prevent greenwashing and bias, offering insights for global policymakers and financial institutions.

👥 読者別の含意

🔬研究者:Provides empirical evidence on ESG data divergence and algorithmic bias in FinTech, useful for further research on AI-driven ESG evaluation.

🏢実務担当者:Highlights risks in using ESG scores for investment and credit decisions, emphasizing the need for robust data governance and bias mitigation.

🏛政策担当者:Informs regulatory discussions on standardizing ESG ratings and regulating AI algorithms to ensure ethical financial practices.

📄 Abstract（原文）

The paper explores how ESG rating frameworks are embedded into FinTech tools such as AI-driven investment systems, credit-assessment algorithms, and automated portfolio managers. While ESG adoption supports more responsible and sustainable investing by improving transparency, accountability, and long-term risk oversight, several significant obstacles still persist. These involve inconsistencies between ESG ratings among providers, a lack of data quality, algorithmic bias, and increasing risk of greenwashing. Empirical evidence shows that the data divergence in ESG and incomplete disclosures may result in making misinformed financial decisions and reinforcing structural disparities, especially in the case of emerging markets. It finds that despite the revolutionary promise of ESG-based FinTech to financial sustainability, it requires better standardisation, reliability of data, and clear algorithmic regulation to reduce bias and deliver genuinely ethical financial results.

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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。