資本市場ベース会計におけるビッグデータとAIの役割
The Role of Big Data and AI In Capital Market Based Accounting (原題)
Masradin Masradin, Syamsuddin Syamsuddin, Neks Triani, Syarifuddin Rasyid, Sahrir Sahrir
🤖 gxceed AI 要約
日本語
本論文はPRISMA 2020に沿った系統的文献レビューにより、資本市場ベース会計におけるビッグデータとAIの役割を検討した。29件の研究をデータ基盤・分析処理・開示報告・ガバナンス保証の4側面に分類し、AVC-GLモデルへ統合。AIは複雑データ処理と意思決定を支援し開示効率を高める一方、データ品質・ガバナンス・人的監督に依存し、生成AIはグリーンウォッシュ等の新リスクを伴うと指摘する。
English
This systematic literature review (PRISMA 2020) examines Big Data and AI in capital-market-based accounting, synthesizing 29 studies into four aspects: data infrastructure, analytics, disclosure/reporting, and governance/assurance, integrated into an AVC-GL model. Big Data broadens information sources while AI supports complex processing and decisions, but effectiveness hinges on data quality, governance, oversight, and human judgment. Generative AI improves disclosure efficiency yet introduces risks such as greenwashing.
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・有報のサステナビリティ開示が進む日本では、AIによる開示作成・保証の効率化とグリーンウォッシュ抑止のガバナンス設計が実務課題となる。本稿のAVC-GLモデルは、日本企業の開示プロセス統制や内部統制・第三者保証の枠組み検討に示唆を与える。
In the global GX context
As ISSB/SSBJ-aligned disclosure and assurance regimes tighten globally, this paper maps how AI and Big Data reshape disclosure production and assurance, and flags greenwashing risk from generative AI. It offers a governance-oriented framework relevant to CSRD/ISSB implementation and audit/assurance debates.
👥 読者別の含意
🔬研究者:AI×会計・開示研究の分類枠組みとAVC-GLモデルを、今後の実証研究の出発点として活用できる。
🏢実務担当者:AI活用時のデータ品質・ガバナンス・人的監督の重要性を踏まえ、開示プロセスとグリーンウォッシュ防止策を設計する際の参考になる。
🏛政策担当者:AI生成開示の信頼性確保と保証制度の設計において、ガバナンス要件の必要性を示唆する。
📄 Abstract（原文）
This study used a Systematic Literature Review (SLR) to examine the role and use of Big Data and Artificial Intelligence (AI) in capital market-based accounting and identify factors influencing the effectiveness of their implementation. The study followed the PRISMA 2020 protocol. From 437 records obtained through eight keyword combinations in the Scopus database, 29 studies were selected after filtering based on the 2016–2026 publication period, Q1–Q4 journal quality, and thematic relevance. The study grouped the literature into four main aspects: data infrastructure, analytical processing, disclosure and reporting, and governance and assurance. These four aspects were then integrated into the AI-Accounting Value Chain and Governance Loop (AVC-GL) model. The findings indicate that Big Data expands information sources for accounting and capital market analysis, while AI helps process complex data and supports decision-making. However, the effectiveness of its implementation is highly dependent on data quality, governance, professional oversight, and human ability to evaluate technological outcomes. The use of generative AI also improves the efficiency of corporate disclosures, but introduces new risks, including greenwashing. This study contributes through the development of a functional taxonomy and AVC-GL model to explain the relationship between technology, governance, information quality, and decision-making in capital markets.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://owner.polgan.ac.id/index.php/owner/article/download/3836/1922first seen 2026-10-06 05:49:59
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