In response to the growing sophistication of economic operations and digital transformation, this paper analyzes the application and reliability of Benford’s Law as a tool for detecting financial fraud in forensic accounting and auditing. Despite its mathematical validity and proven effectiveness, this method faces certain practical limitations, including the absence of standardized approaches to interpreting results, inconsistency in statistical indicators, and insufficient adaptation to the specific requirements of forensic accounting and auditing. This work systematizes these limitations and reviews contemporary practice offering solutions to the identified challenges. Building upon these findings, the research proposes a comprehensive four-stage methodology that integrates Benford’s Law with data mining techniques, unsupervised learning algorithms, and Bayesian probability scoring to overcome traditional single-metric limitations and reduce false positive rates. This integrated approach demonstrates significant potential for advancing forensic examination capabilities and enabling more informed decision-making in financial fraud detection.