<![CDATA[The purpose of this study is to systematically review the existing literature on the role of AI in preventing and uncovering financial statement fraud in the auditing process, particularly in identifying anomaly patterns that are often difficult for auditors to detect manually. The study also aims to identify the challenges faced in implementing AI in auditing processes. The method used in this research is the Systematic Literature Review (SLR), where relevant literature is identified, selected based on inclusion and exclusion criteria, analyzed, and interpreted to provide a comprehensive overview of the topic. The results show that AI holds significant potential for detecting fraud and enhancing the effectiveness of the audit process. However, several challenges must be addressed, such as data quality and availability, limitations regarding the interpretability and transparency of AI models, resistance to change, and a lack of understanding of how AI functions. Additionally, data security and privacy aspects must be considered to ensure that AI implementation is carried out optimally and responsibly]]>
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