Artificial Intelligence in Auditing and Financial Accounting: Implications for Information Reliability, Accountability, and Investor Trust

Abstract

This article examines how Artificial Intelligence (AI) affects auditing and financial accounting, with particular attention to information reliability, accountability, and investor trust. Using a conceptual literature review, the study synthesizes research on AI-enabled audit analytics, machine learning, robotic process automation, natural language processing, governance, and professional judgment. The analysis indicates that AI can improve the timeliness, consistency, coverage, and analytical depth of accounting and audit procedures by processing large datasets, identifying anomalies, supporting reconciliations, and assisting risk assessment. However, these benefits do not automatically guarantee reliable reporting. Data quality, model bias, limited explainability, cyber risk, automation bias, and weak controls may create new forms of error and uncertainty. Accountability therefore requires clear responsibility for data, model design, validation, human review, documentation, and escalation of exceptions. Investor trust is most likely to be strengthened when AI use is transparent, appropriately governed, independently evaluated, and accompanied by credible assurance and disclosure. The article proposes an integrated framework connecting AI capability, governance controls, information quality, accountability, and investor trust. It concludes that AI should augment—not replace—professional scepticism and judgment, while organizations should establish risk-based governance throughout the AI lifecycle.