Machine Learning-Based Detection of Accounting Irregularities in Corporate Financial Reports

Abstract

The detection of accounting irregularities in corporate financial reports has become a complex systems problem that extends beyond traditional ratio analysis and accrual modeling. Machine learning methods are increasingly deployed to identify anomalous reporting behavior in large populations of public firms, yet their performance depends on the design of data infrastructures, the selection of textual and numeric features, the management of imbalanced data, and the integration of model outputs into audit and regulatory workflows. This paper presents a system-level examination of machine learning-based detection of accounting irregularities. It analyzes the structural trade-offs between predictive accuracy, interpretability, false alert burden, and operational cost. It further considers how narrative disclosures, particularly risk factor sections and management discussion, can be transformed into semantic features that complement structured financial variables. The discussion addresses governance requirements, including data lineage, model refresh cycles, fairness, robustness, and the explainability expectations of auditors, boards, and regulators. Rather than treating detection as a purely algorithmic task, the paper frames it as a socio-technical infrastructure in which machine learning components interact with organizational expertise, regulatory standards, and market incentives. It concludes by identifying forward-looking research and policy directions for building accountable, sustainable, and operationally credible machine learning systems in financial reporting oversight.