Artificial intelligence vs traditional methods in auditing: A comparative analysis of efficiency, accuracy, and practical restrictions.

Abstract

The incorporation of artificial intelligence (AI) into the auditing industry is the subject of this research project, which focuses on how AI will impact traditional auditing procedures, potential adoption difficulties, and how AI will eventually improve audit operations.  Using a mixed-methods approach, the study investigates how AI is changing the auditing industry using qualitative theme evaluation and quantitative survey analysis. Key findings show that by automating tedious procedures and analyzing big information, AI greatly increases audit efficiency and accuracy. Constraints are also identified by the report, including the requirement for a large financial commitment, the lack of qualified AI specialists, and worries about data confidentiality and safety. Notwithstanding these drawbacks, AI has the potential to revolutionize auditing procedures; several participants acknowledged that AI can improve fraud detection, anomaly identification, and real-time monitoring. The study concludes that, even if artificial intelligence (AI) is expected to dominate auditing in the future, human oversight and involvement are still necessary to guarantee accuracy, ethical concerns, and the overall quality of the audit. Strong data security standards, more funding for auditor AI training, and ongoing investigation into the moral consequences of using AI in auditing are among the recommendations. This study adds to the expanding literature of research on artificial intelligence in the auditing and accounting industries and provides useful information for regulators, auditors, and accounting companies. The results imply that the best strategy for negotiating the changing audit scenario may involve a hybrid paradigm in which AI augments human auditors.