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Management and Economics OPEN ACCESS

Digital Transformation of Financial Reporting: AI-Driven Accuracy and Transparency in Corporate Disclosures

Mikhail Kobanenko
Master of Business Administration in Finance, Metropolitan College of New York New York, USA
DOI Crossref DOI 10.37547/tajmei/Volume07Issue11-03
tajmei 2025
VOL. 7 / NO. 11 NOVEMBER
FULL TEXT PP. 17-25
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VOLUME 7
ISSUE 11
YEAR 2025
PAGES 17-25
RESEARCH OVERVIEW

Abstract

This study explores the reconfiguration of financial reporting practices under the influence of artificial intelligence, which enhances both the precision and lucidity of corporate information disclosure. The investigation gains its relevance from the international trend toward automation and data-centric approaches in accounting and audit operations, reflecting the escalating intricacy of financial datasets. The originality of the paper lies in a thorough evaluation of how intelligent technologies—namely, machine learning, robotic automation, and natural language systems—redefine the credibility of reports and the consistency of disclosures. The research delineates the operational channels through which AI strengthens anomaly recognition, refines data workflows, and facilitates ongoing audit processes. Emphasis is placed on governance concerns, particularly the transparency and interpretability of AI mechanisms that help solidify confidence among stakeholders. The core aim is to assess AI’s impact on the qualitative dimensions of financial reporting and its broader implications for corporate responsibility. By employing analytical and comparative approaches, the study interprets empirical evidence drawn from contemporary investigations and expert surveys. In conclusion, the findings present AI as an accelerant toward a heightened benchmark of reporting accuracy and openness—informing regulators, executives, and auditors striving for responsible AI adoption in financial communications.

RESEARCH TOPICS

Keywords

artificial intelligence financial reporting automation transparency anomaly detection natural language processing
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References

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