<![CDATA[Fraud detection in accounting and auditing has evolved significantly due to technological advancements and regulatory developments. This study reviews existing literature on the impact of artificial intelligence, big data analytics, and organizational ethics in strengthening fraud detection. Using a comprehensive methodology, relevant sources from Google Scholar, JSTOR, ScienceDirect, and other academic databases were analyzed to identify key trends and challenges in forensic auditing. Findings indicate that machine learning algorithms significantly enhance fraud detection accuracy, while organizational commitment to ethical standards plays a crucial role in fostering a transparent audit environment. Regulatory frameworks, although essential, must strike a balance to avoid undue constraints on auditors. The study also highlights the necessity of continuous auditor training to optimize the application of emerging technologies in fraud detection. These insights underscore the importance of integrating technological advancements with ethical and regulatory considerations to improve fraud detection efficiency. Future research should focus on refining AI-based audit tools and developing tailored regulatory frameworks that promote both compliance and audit independence.]]>
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