AI and Financial Reporting Quality: An Empirical and Theoretical Assessment of Accuracy, Timeliness, Transparency, and Assurance

Abstract

Artificial intelligence (AI) is quietly transforming accounting and financial reporting, by letting organizations automate those repeatable tasks, scan huge piles of financial plus non-financial information, spotlight strange transactions, and in general help audit and assurance chores. As many organizations shift from the old rule-based accounting setups toward machine learning, natural language processing predictive analytics, and other AI enabled tools, a not so small research question shows up: does the sheer technological sophistication actually end up meaning better financial reporting. In this study we look at the link between AI and financial reporting quality, while paying close attention to things like accuracy and reliability, relevance, timeliness, transparency, fraud detection, error reduction, professional judgement internal controls, and audit quality. The discussion leans on information processing theory and the technology organization environment perspective, and it builds a conceptual model where AI capability is expected to affect financial reporting quality through automated processing anomaly detection continuous monitoring analytical capacity and decision support. But this influence is not free governance data quality human expertise and organizational readiness are treated as conditions that shape how well those mechanisms work sort of like the engine needs fuel and steering at the same time. The paper uses a quantitative research design for the empirical part and suggests using structured questionnaires given to accounting professionals, auditors, finance managers and reporting specialists and where possible adding archival indicators of reporting quality. For the analysis descriptive statistics are more suitable than reliability and validity checks, correlation analysis and multiple.

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