Application of Machine Learning in Accounting Fraud Detection: A Systematic Literature Review
(Penerapan Machine Learning dalam Deteksi Kecurangan Akuntansi: Tinjauan Literatur Sistematis)
DOI:
https://doi.org/10.20625/theosyn.v1i3.060Keywords:
Accounting Fraud, Fraud Detection, Machine Learning, Systematic Literature ReviewAbstract
This study aims to examine the application of machine learning in detecting accounting fraud using a Systematic Literature Review (SLR) approach. The research seeks to understand how machine learning technology enhances audit effectiveness and improves the accuracy of identifying suspicious financial reports. The literature search was conducted through databases such as Google Scholar, Scopus, and IEEE Xplore, covering publications from 2015 to 2025. From an initial pool of 50 studies, 20 were screened for relevance, and 8 core articles were analyzed in depth. The results reveal that algorithms such as Random Forest, Support Vector Machine, and Neural Network achieve high accuracy in detecting financial anomalies. However, the success of implementation largely depends on data quality, model interpretation, and the readiness of human resources to adopt the technology ethically and responsibly.
Bahasa Indonesia
Penelitian ini bertujuan untuk mengkaji penerapan machine learning dalam mendeteksi kecurangan akuntansi melalui pendekatan Systematic Literature Review (SLR). Kajian ini dilakukan untuk memahami sejauh mana teknologi pembelajaran mesin mampu meningkatkan efektivitas audit dan akurasi dalam identifikasi laporan keuangan yang mencurigakan. Proses penelitian melibatkan penelusuran literatur dari berbagai basis data seperti Google Scholar, Scopus, dan IEEE Xplore dengan periode publikasi antara 2015 hingga 2025. Dari 50 artikel yang ditemukan, 20 artikel diseleksi lebih lanjut, dan 8 artikel utama dianalisis secara mendalam. Hasil penelitian menunjukkan bahwa algoritma seperti Random Forest, Support Vector Machine, dan Neural Network memiliki tingkat akurasi tinggi dalam mendeteksi anomali keuangan. Namun, keberhasilan implementasi sangat bergantung pada kualitas data, interpretasi hasil, dan kesiapan sumber daya manusia dalam mengadopsi teknologi secara etis dan bertanggung jawab.
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