持続可能な金融の文脈におけるESGリスク管理へのFinTech活用に関する考察
Reflections on the Application of FinTech in ESG Risk Management under the Background of Sustainable Finance (原題)
Jie-Qiong Li
🤖 gxceed AI 要約
日本語
持続可能な金融の拡大に伴い、ESGリスク管理は金融機関の与信管理の中核となっている。本稿はビッグデータ・ブロックチェーン・AIといったフィンテックが、定量評価・動的モニタリング・閉ループ管理を通じてESGリスクガバナンスの精度を高め得ると論じる。一方でデータ品質の格差、アルゴリズムの構造的バイアス、評価枠組みの標準化不足、規制の遅れといった実装上の障壁を整理し、中国の金融機関向けに標準化・デジタル化されたESGリスク管理体制の構築方策を提示する。
English
As sustainable finance expands in China, ESG risk management has become central to financial institutions' compliance and green credit control. This paper argues that fintech—big data, blockchain, and AI—can enable quantitative assessment, dynamic monitoring, and closed-loop ESG risk governance, improving precision over static, manual disclosure-based systems. It then identifies practical barriers (data quality gaps, algorithmic bias, non-standardized ESG frameworks, lagging regulation) and proposes optimization pathways for building standardized, digitalized ESG risk-control systems in Chinese financial institutions.
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
中国のグリーン金融・ESGリスク管理におけるフィンテック活用の実務論であり、日本のSSBJ開示・統合報告書対応や金融機関のESG与信管理を考える上でも、データ品質・アルゴリズムバイアス・評価標準化といった共通課題への示唆がある。
In the global GX context
This paper speaks to the global push for digitalized ESG risk infrastructure under TCFD/ISSB and CSRD, where data quality, algorithmic bias, and framework standardization are shared challenges for financial institutions. It offers a China-context view on how fintech can operationalize ESG risk management, complementing disclosure-focused scholarship.
👥 読者別の含意
🔬研究者:AI×ESGリスク評価の実装障壁（データ品質・アルゴリズムバイアス・標準化）を整理する枠組みとして参照できる。
🏢実務担当者:ESG与信・リスク管理のデジタル化を検討する金融機関が、フィンテック導入時の留意点と最適化経路を把握できる。
🏛政策担当者:ESG評価枠組みの標準化と規制整備の遅れがフィンテック活用を妨げる点を、政策設計の論点として参照できる。
📄 Abstract（原文）
As China's green financial system continues to improve and sustainable finance steadily expands, it has effectively promoted the green and low-carbon transformation and upgrading of the domestic capital market. Against the backdrop of the routine development of green investment and financing activities, environmental, social, and governance (ESG) risk management has become a core tool for financial institutions to ensure compliance and control green credit risks, directly determining the quality of risk management across various green financial services. Traditional ESG assessment systems primarily rely on static corporate disclosures and manual expertise for risk evaluation, resulting in a relatively coarse overall approach that struggles to adapt to the new industry landscape following the full implementation of low-carbon policies. These systems commonly suffer from inherent shortcomings such as delayed data updates, strong subjectivity in human judgment, reactive risk mitigation, and insufficient coverage of risk dimensions, making them inadequate to meet today’s demand for refined, high-quality green finance development. Leveraging cutting-edge fintech technologies—including big data, blockchain, and artificial intelligence—financial institutions can achieve quantitative risk assessment, dynamic monitoring, and end-to-end closed-loop management of ESG risks, significantly enhancing the intelligence and precision of green risk governance. However, in practice, multiple real-world barriers still hinder the effective application of fintech in ESG risk management. Issues such as significant disparities in data quality, structural biases in algorithms, lack of standardization in industry-wide ESG evaluation frameworks, and lagging regulatory evolution substantially diminish the enabling power of digital technologies. Based on current industry practices, this paper systematically identifies key application scenarios of fintech in ESG risk management, deeply analyzes critical implementation bottlenecks, and proposes targeted optimization pathways to provide practical guidance for Chinese financial institutions in building standardized, digitalized ESG risk control systems.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://jmfrm.org/index.php/ojs/article/download/30/31first seen 2026-11-12 05:50:37
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