Deteksi Kecurangan Laporan Keuangan Melalui Model Beneish M-Score dan Behavioral Analytics: Studi Literatur Sistematis
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Abstract
Penelitian ini bertujuan mengkaji perkembangan studi deteksi kecurangan laporan keuangan periode 2020–2025, mengevaluasi efektivitas Fraud Triangle dan pengembangannya, menganalisis Beneish M-Score (akurasi dan keterbatasan), mengeksplorasi peran behavioral analytics dan machine learning, serta mengidentifikasi implikasi POJK No. 12 Tahun 2024 bagi strategi anti-fraud lembaga jasa keuangan di Indonesia. Metode yang digunakan adalah Systematic Literature Review (SLR) dengan pendekatan kualitatif deskriptif dan protokol PRISMA. Hasil sintesis menunjukkan adanya pergeseran dari pendekatan konseptual menuju integrasi teori fraud, model rasio (khususnya Beneish M-Score), serta pendekatan data-driven. Fraud triangle tetap dominan namun banyak diperluas menjadi diamond/pentagon/hexagon. Beneish M-Score efektif sebagai early warning tetapi sensitif terhadap konteks industri dan berisiko menghasilkan false positive/false negative sehingga lebih tepat dalam kerangka multi-metode. Behavioral analytics dan machine learning menunjukkan potensi deteksi yang lebih prediktif melalui penggabungan data keuangan dan non-keuangan. POJK 12/2024 menyediakan kerangka implementasi yang memperkuat pilar pencegahan, deteksi, investigasi, serta pemantauan dan evaluasi anti-fraud.
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