Articles
DOI DOI: 10.5281/zenodo.18435898

AI-Assisted Narrative Reporting in U.S. Filings Audit Quality, Disclosure Readability, and the Emergence of Algorithmic Boilerplate

Abstract

The fast adoption of generative artificial intelligence (AI) in corporate reporting
systems has led to quicker development of narrative disclosures which appear
in U.S. SEC filings through Management’s Discussion and Analysis (MD&A)
and risk factor disclosures. The use of AI for drafting helps create error-free
documents which read well but it could lead to the development of standardized
legal language which becomes less effective for decision-making purposes. The
research develops a framework which studies AI implementation in narrative
reporting to determine its effects on three essential variables which include
audit quality results and disclosure clarity and corporate narrative content
uniqueness and semantic alignment. The research uses difference-in-differences
and event-study methods to analyze AI reporting adoption through firm-specific
signals which are extracted from 10-K section text and audit-quality indicators
(restatements and internal control material weaknesses and audit fees and
audit report lag). The research indicates that AI implementation leads to better
understanding of plain-English content but simultaneously boosts the similarity
between peers and the duration of information retention within organizations
which results in a separation between text clarity and information value. The
research provides findings which affect how auditors evaluate risks and how
audit committees monitor activities and what regulatory bodies should do to
prevent companies from making vague risk statements.

How to Cite

deHaan, E. (2026). AI-Assisted Narrative Reporting in U.S. Filings Audit Quality, Disclosure Readability, and the Emergence of Algorithmic Boilerplate. Transnational Academic Journal of Economics, 3(1), 131–142. https://doi.org/10.5281/zenodo.18435898

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