ESGと財務報告における人工知能
Artificial Intelligence in ESG and Financial Reporting (原題)
Mansi Sudhir Kadam
🤖 gxceed AI 要約
日本語
本論文は、AIがESG情報と財務情報の収集・検証・分析・開示をどう強化できるかを検討する。IFRS S1・S2、IFRSサステナビリティ開示タクソノミー、インドのBRSRおよびBRSR Coreに着目し、AIが適時性・一貫性・拡張性・意思決定有用性を高める一方、データ品質・ガバナンス・説明可能性・人的監督・サイバーセキュリティに依存すると指摘する。AIは自律的代替ではなく、統制された報告技術として扱うべきだと結論づけ、データ系譜・モデルガバナンス・保証統制を備えた統合アーキテクチャを提言する。
English
This paper examines how AI can strengthen the link between ESG and financial reporting through data processing, anomaly detection, disclosure preparation, forecasting and digital reporting. Drawing on IFRS S1/S2, the IFRS Sustainability Disclosure Taxonomy, India's BRSR and BRSR Core, it finds AI improves timeliness, consistency and scalability, but depends on data quality, governance, explainability and human oversight. It recommends a controlled AI-enabled reporting architecture with data lineage, model governance and assurance controls.
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基準や有報でのサステナビリティ開示が本格化する日本企業にとって、ESGと財務報告を統合するAI活用アーキテクチャは、開示プロセスの効率化と保証対応の両面で実務的示唆が大きい。IFRS S1/S2準拠を見据えたデータガバナンス設計の参考になる。
In the global GX context
As ISSB standards (IFRS S1/S2) and CSRD raise the volume and frequency of sustainability disclosure, this paper speaks directly to the global challenge of connecting ESG and financial reporting through AI. Its emphasis on model governance, data lineage and assurance controls aligns with emerging expectations from regulators and auditors worldwide, and the BRSR case adds a major emerging-market perspective to disclosure-infrastructure scholarship.
👥 読者別の含意
🔬研究者:AI×ESG開示の統合アーキテクチャと統制要件を整理した枠組みとして、実証研究の出発点にできる。
🏢実務担当者:ESG・財務報告の統合に向けたAI導入時に、データ系譜・モデルガバナンス・人的レビューを設計要件として組み込む指針になる。
🏛政策担当者:IFRS S1/S2やBRSR準拠の開示制度設計において、AI利用時の説明可能性・保証・説明責任の枠組みを検討する材料になる。
📄 Abstract（原文）
Abstract Artificial Intelligence (AI) is increasingly becoming an enabling technology for the collection, validation, analysis and communication of environmental, social and governance (ESG) information and financial information. At the same time, expanding sustainability disclosure requirements are increasing the volume, variety and frequency of non-financial information that organisations must manage. This paper examines how AI can strengthen the relationship between ESG reporting and financial reporting by improving data processing, anomaly detection, disclosure preparation, forecasting, risk identification and digital reporting. The study uses a qualitative, descriptive and exploratory research design based on secondary sources, including international reporting standards, Indian regulatory publications and recent academic literature. Particular attention is given to IFRS S1, IFRS S2, the IFRS Sustainability Disclosure Taxonomy, India's Business Responsibility and Sustainability Reporting (BRSR) framework and BRSR Core. The findings indicate that AI can improve reporting timeliness, consistency, scalability and decision usefulness, but its effectiveness depends on data quality, governance, explainability, human oversight, cybersecurity and clear accountability. AI should therefore be treated as a controlled reporting technology rather than an autonomous substitute for professional judgement. The paper recommends an integrated AI-enabled reporting architecture with strong data lineage, model governance, human review, assurance controls and ethical safeguards.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.5281/zenodo.23191011first seen 2026-10-08 04:53:19
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