サステナブルファイナンス発展を背景としたAI駆動型財務分析のリスク管理における機会と実務的課題
Opportunities and Practical Challenges of Risk Management for AI-Driven Financial Analysis Under the Background of Sustainable Finance Development (原題)
Guan-Jie Li
🤖 gxceed AI 要約
日本語
サステナブルファイナンスの制度基盤が原則から厳格な制約へ移行する中、AIによる財務分析・リスク管理の変化を論じる。ESGデータ統合、気候移行リスクの定量化、カーボンファイナンス価格付けへのAI適用を整理し、データ品質・モデル透明性・アルゴリズム収束・制度対応の遅延といった課題を指摘。データガバナンス、モデル規制、規制調整の三面から改善策を提言し、技術革新の促進とシステムリスク防止の動的均衡を求める。
English
Examines how AI is reshaping financial risk management as sustainable finance shifts from principles to hard limits. Reviews AI applications in ESG data integration, climate transition risk quantification, and carbon finance pricing, while flagging data quality, model transparency, algorithmic convergence, and institutional latency constraints. Proposes reforms across data governance, model regulation, and regulatory coordination, calling for balance between innovation incentives and systemic risk prevention.
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
SSBJ基準の適用開始と有報でのサステナビリティ開示義務化が進む日本では、AIを用いたESGデータ統合や気候移行リスク定量化の実務導入が焦点となる。本稿のデータガバナンス・モデル規制の提言は、金融庁のAIガバナンス議論や企業の開示高度化対応に示唆を与える。
In the global GX context
As ISSB standards are adopted across jurisdictions and the EU AI Act takes effect, this paper speaks directly to the global convergence of AI governance and climate disclosure regimes. Its framing of data governance and model regulation adds to the debate on how AI can be trusted within TCFD/ISSB-aligned transition-risk assessment and carbon finance.
👥 読者別の含意
🔬研究者:Provides a structured agenda on AI-enabled climate risk and ESG analytics, highlighting interpretability and institutional-latency gaps worth empirical follow-up.
🏢実務担当者:Useful for sustainability and risk teams scoping AI tools for ESG data integration and transition-risk quantification, with governance checkpoints to build in.
🏛政策担当者:Offers a three-pillar (data, model, coordination) framing for regulating AI in sustainable finance without stifling innovation.
📄 Abstract（原文）
The institutional architecture of sustainable finance is moving from principles to hard limits. The artificial intelligence is entrenched in the financial analysis, and it is changing the reasoning of risk management. This article explains the application of conventional AI to integrating ESG data, quantifying climate transition risk, pricing carbon financing, and evaluating the effectiveness of AI in risk management. We also discuss some of the restrictions including data quality, model transparency, algorithm convergence and institutional latency. The effectiveness of AI-enabled sustainable finance risk management depends on the comprehensiveness of the data infrastructure, the level of model interpretability attained and the flexible adaption of the governance framework. The green development framework of the "15th Five-Year Plan" has been fully launched. The EU "Artificial Intelligence Act" has come into effect in August 2024, and the ISSB standards have been consecutively implemented in several nations. The institutional environment accelerates the reconfiguration. Based on this, this study proposes improvement suggestions from the three aspects of data governance, model regulation and regulatory coordination, and calls for seeking the dynamic balance between technological innovation incentives and systemic risk prevention.
🔗 Provenance — このレコードを発見したソース
- semanticscholar http://ijwer.org/index.php/ojs/article/download/104/103first seen 2026-10-19 05:25:58 · last seen 2026-11-06 05:33:22
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