Digital Transformation and Financial Reporting Quality in Corporate Finance: Evidence from Internal Control Practices using Artificial Intelligence

Abstract

This paper develops an integrative explanation of how digital transformation can improve financial reporting quality through artificial intelligence (AI)-enabled internal control practices. Rather than treating technology adoption as a direct guarantee of better reporting, the analysis positions digital capability as an enabling condition whose value depends on process design, data governance, control ownership, and human review. The study combines an integrative conceptual review with a structured evidence-coding dataset of 30 cited sources published between 1977 and 2026. The dataset records publication period, source class, evidence design, primary literature stream, and non-exclusive relevance to digital transformation, AI and automation, internal control, financial reporting quality, and governance or ethics. It is used for descriptive transparency rather than effect estimation. Of the 30 sources, 24 are peer-reviewed articles and six are official standards or governance frameworks; 16 are review or conceptual works, eight provide empirical evidence, and six are standards or frameworks. Evidence is organized around recurring corporate-finance processes: invoice verification, bank reconciliation, journal-entry and period-close controls, management information system reporting, continuous monitoring, and audit documentation. The synthesis shows that AI can increase transaction coverage, accelerate matching and exception detection, strengthen traceability, and shorten reporting cycles. These gains improve relevance, faithful representation, timeliness, comparability, verifiability, and understandability only when AI is embedded in an effective system of internal control. Poor-quality data, weak access controls, opaque models, automation bias, model drift, cyber exposure, and ungoverned generative AI can instead scale errors and weaken accountability.

The paper proposes a process-based framework in which AI-enabled control capability strengthens internal control over financial reporting, which then mediates the relationship between digital transformation and reporting quality. It also presents testable propositions, an implementation roadmap, descriptive figures, a source-level dataset, and illustrative measures for future empirical research. The central conclusion is that AI should be governed as a control capability, not deployed merely as a productivity tool.