<![CDATA[Notwithstanding rigorous oversight in the Indonesian capital market, the manipulation of financial reports continues to occur. This study examines the potential for employing machine learning (ML) models, which utilize linguistic features and financial ratios, in effectively detecting deception or manipulation. Drawing upon publicly listed Indonesian companies as the samples, this research validates the predictive capabilities of the Beneish M-Score, confirms the occurrence of negative language in fraudulent reports, and demonstrates the superiority of the Gradient Boosting ML model in identifying anomalies within financial and textual data. The study distinctively adapts to Indonesian-language annual reports, thereby addressing a gap in the linguistic-based fraud detection literature. These findings not only advance our comprehension of how linguistic features and financial ratios provide practical tools for fraud detection, thereby preparing the academic and professional community in this domain.]]>
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