Auditing the Algorithm: Policy for AI-Driven Financial Reporting

Abstract

The introduction of artificial intelligence (AI) in financial reporting has revolutionized the auditing practice by improving accuracy, efficiency, and analysis. There is, however, a growing concern about the transparency, accountability, and regulation of machine learning algorithms, which are increasingly becoming relevant. The paper analyzes the policy implications of AI-assisted financial reporting and proposes a framework for auditing algorithmic systems used in financial decision-making. The paper examines the available literature and policy recommendations using qualitative and quantitative approaches to determine significant risks, including algorithmic bias, lack of explainability, unfavorable data governance, and the absence of human participation in the AI-nearest financial statements. The results indicate the necessity of standard auditing procedures designed specifically for AI systems, including model validation, documentation transparency, and continuous monitoring. By providing policy-oriented recommendations to enhance trust, fairness, and accuracy in AI-assisted financial reporting, the study contributes to current debates in accounting, auditing, and technology governance.