<![CDATA[Financial statement fraud remains a persistent challenge to corporate integrity and global market stability, with annual losses estimated at over $4.5 trillion. Traditional auditing methods often fail to uncover complex and technologically sophisticated schemes, creating a critical need for forensic accounting as a complementary investigative approach. This study investigates the role of forensic accounting in detecting financial statement fraud by integrating a mixed-methods design. Qualitative data were obtained through case studies of high-profile fraud incidents (Enron, WorldCom, HealthSouth, and Lehman Brothers) and semi-structured interviews with 30 forensic accounting professionals. Quantitative evidence was collected from surveys of 100 finance practitioners, assessing the perceived effectiveness of forensic accounting techniques. Thematic analysis revealed that forensic accountants employ analytical procedures, ratio analysis, data mining, and advanced digital tools to identify anomalies overlooked by conventional audits. Descriptive and inferential statistics confirmed that over 70% of respondents considered forensic practices significantly more effective in fraud detection compared to traditional auditing. Findings also highlight emerging challenges, including organizational resistance, reliance on historical data, and the growing complexity of cyber-enabled fraud. This research contributes to the literature by demonstrating how the integration of advanced analytics, machine learning, and blockchain applications can enhance forensic accounting practices, bridging a critical gap between conventional auditing and proactive fraud prevention. The study provides both theoretical and practical implications, underscoring the necessity of embedding forensic accounting into governance structures to strengthen transparency, investor confidence, and long-term organizational resilience..]]>
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