Leveraging Artificial Intelligence in Financial Reporting: Applications, Impacts, and Challenges
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Artificial intelligence (AI) is increasingly embedded in the way organizations prepare, verify, and disclose financial information. This paper examines how AI affects financial reporting and how firms are leveraging it across the reporting cycle, from data capture and reconciliation to disclosure drafting, fraud detection, and audit support. Using a desk-based synthesis of recent academic literature and large-scale industry survey data, the paper traces the main technologies in use — machine learning, natural language processing, robotic process automation, and generative AI — and evaluates their effect on the accuracy, timeliness, and reliability of financial reports. The synthesis shows that AI adoption in financial reporting has moved from experimentation toward mainstream use, that generative AI is already measurably present in narrative disclosures, and that machine-learning models outperform traditional rule-based methods in detecting financial statement fraud. At the same time, the literature consistently flags persistent challenges: algorithmic bias, limited explainability, data privacy, regulatory uncertainty, and a skills gap among finance professionals. The paper concludes that AI is best understood as a complementary capability that reshapes finance roles and reporting workflows rather than a wholesale replacement for professional judgment, and it outlines directions for future research on governance and disclosure quality.
Keywords
Accounting Automation, Artificial Intelligence, Disclosure Quality, Financial Reporting, Fraud Detection, Generative Ai, Machine Learning, Robotic Process Automation