<![CDATA[This study examines the effectiveness of artificial intelligence in forensic accounting for detecting financial fraud within the digital economy. Using a quantitative machine learning approach, the research analyzes 2,314 anonymized digital transaction records from a digital-based Savings and Loan Cooperative in Central Java. Transaction data extracted from accounting information systems were cleaned and analyzed using random forest, logistic regression, and support vector machine algorithms. The results show that AI integration improves fraud detection accuracy up to 91.8%, with transaction frequency, late payments, and loan application patterns identified as key anomaly indicators. The study concludes that AI-based forensic accounting strengthens internal control systems and contributes theoretically through replicable machine learning fraud detection models, while offering practical implications for forensic auditors and modern financial governance policies.]]>
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