<![CDATA[Financial statement fraud undermines the integrity of global financial markets and poses critical challenges to corporate governance. This study conducts a narrative review to synthesize existing knowledge on fraud detection techniques and slished within the last two decades addressing fraud detection models, governance frameworks, and regional variations. Findings reveal that traditional statistical methods such as the Beneish M-Score and Altman Z-Score provide foundational tools but are increasingly supplemented by machine learning and artificial intelligence models, which achieve higher accuracy rates in detecting anomalies. Forensic accounting and data mining further enhance detection capabilities. Governance mechanisms, particularly board independence, audit committees, auditor rotation, and whistleblower protections, emerge as essential for reducing fraud incidence, with regulatory oversight reinforcing these practices in developed markets. However, emerging economies face significant challenges due to weaker institutions and limited adoption of advanced technologies, resulting in higher fraud prevalence. Discussion highlights systemic factors such as regulatory gaps, market pressures, and organizational culture as key contributors to persistent fraud. Policy reforms, technological innovations, and future research integrating human and computational dimensions are recommended to build adaptive frameworks. This review underscores the urgency of combining governance reforms and AI-driven detection systems to safeguard financial reporting integrity globally.]]>
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