The Role of AI in Detecting Financial Anomalies and Supporting Auditors in Corporate Reporting
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The rapid advancement of artificial intelligence (AI) presents transformative opportunities for the auditing profession, particularly in the detection of complex financial fraud. As traditional auditing methods increasingly struggle to keep pace with escalating data volumes and sophisticated financial manipulation, high-profile corporate scandals—such as Enron and Wirecard— underscore the urgent need for robust anomaly detection systems. This study investigates the integration of AI as an auxiliary tool designed to enhance, rather than replace, human professional judgment in corporate financial auditing. Utilizing a mixed-method approach, the research qualitatively analyzes the Enron and Wirecard accounting scandals while quantitatively applying the Beneish M-score model to their pre-collapse historical financial data. The empirical findings demonstrate that algorithmic models successfully identified critical indicators of earnings manipulation several years prior to the official bankruptcies of both firms. However, the study also highlights significant implementational challenges, including the risks of automation bias and the inherent inability of AI to replicate human ethical reasoning and professional skepticism. Ultimately, this research concludes that while AI serves as a powerful diagnostic assistant capable of processing massive datasets, its successful deployment requires seamless integration into professional workflows, ensuring that technology empowers rather than undermines the indispensable role of the human auditor.
Keywords
Algorithmic Anomaly Detection, Artificial Intelligence (AI), Auditing, Beneish M-Score, Corporate Reporting, Financial Fraud Detection, Professional Skepticism