マルチモーダルAIと企業データインテリジェンスによる財務・サステナビリティ統合報告
Integrated Financial and Sustainability Reporting through Multimodal AI and Corporate Data Intelligence (原題)
Murali Krishna Pasupuleti
🤖 gxceed AI 要約
日本語
財務諸表・経営者コメント・サステナビリティ指標・気候リスク証拠・運用テレメトリ・ガバナンス記録をマルチモーダルAIで連結する研究アーキテクチャを提示。報告品質を意味的一貫性・因果妥当性・時間比較可能性・モデル汎化・来歴・制御性の関数として捉え、因果推論・表現学習・MLOps・政策対応保証を一つの分析ライフサイクルに統合する。不確実性下の重要性判断や監査対応の開示生成まで射程に入れる。
English
This monograph proposes a research architecture linking financial statements, management commentary, sustainability metrics, climate-risk evidence and operational telemetry via multimodal AI. It frames reporting quality as a joint function of semantic consistency, causal validity, temporal comparability, provenance and controllability, integrating causal inference, MLOps and policy-aware assurance into one lifecycle. It targets audit-ready, traceable disclosure generation across heterogeneous regulatory contexts.
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・統合報告書の連結が進む日本企業にとって、財務と非財務をAIで統合し保証可能にする設計は、開示インフラ整備と監査対応の実務指針になり得る。データ来歴や説明可能性への言及は、日本企業が国際投資家対応で求められる信頼性確保に直結する。
In the global GX context
As ISSB/SSBJ adoption and CSRD raise assurance expectations, this architecture speaks directly to the global shift from document production to data-integrated, auditable disclosure. Its emphasis on provenance, causal validity and explainability addresses the assurance gap that TCFD/ISSB-aligned reporting faces worldwide, including in emerging markets with uneven regulatory capacity.
👥 読者別の含意
🔬研究者:AI×ESG開示の統合研究設計として、因果推論・マルチモーダル学習・保証を横断する方法論的枠組みを提供する。
🏢実務担当者:財務・非財務データを連結し監査対応の開示を生成する仕組みづくりの設計指針として活用できる。
🏛政策担当者:SSBJ/ISSB準拠の開示インフラと保証制度を設計する際、データ来歴・説明可能性要件の参考になる。
📄 Abstract（原文）
Abstract Integrated financial and sustainability reporting is increasingly a data-integration, inference and assurance problem rather than a document-production problem. This monograph develops a research architecture for linking financial statements, management commentary, sustainability metrics, climate-risk evidence, operational telemetry and governance records through multimodal artificial intelligence and corporate data intelligence. The framework treats reporting quality as a joint function of semantic consistency, causal validity, temporal comparability, model generalization, provenance and controllability. It combines statistical explanation, causal inference, multimodal representation learning, scalable data engineering, reproducible MLOps and policy-aware assurance into a single analytical lifecycle. Particular attention is given to materiality under uncertainty, treatment-effect reasoning for corporate interventions, scenario-sensitive risk measurement, cross-modal evidence fusion, lineage-preserving data pipelines, explainability, cybersecurity and human review. The resulting design supports research outputs that range from causal reporting models and confidence-aware disclosure metrics to governance protocols, assurance artefacts and decision dashboards. Its global orientation considers heterogeneous regulatory capacity and data maturity across South Asia, Europe, Africa and the Americas. The book concludes with an invention-oriented system architecture and claim concepts for a traceable multimodal reporting intelligence platform that joins evidence extraction, causal reconciliation, integrated performance analysis and audit-ready disclosure generation. Keywords integrated reporting, sustainability reporting, financial reporting, multimodal artificial intelligence, corporate data intelligence, ESG analytics, double materiality, causal inference, climate finance, natural capital, corporate governance, explainable AI, assurance, data lineage, model risk, MLOps, data provenance, semantic interoperability, anomaly detection, scenario analysis, responsible AI, auditability, decision intelligence
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.62311/nesx/rb58ag-978-81-68314-42-9first seen 2026-09-25 04:47:37
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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。