SYSTEMATIC LITERATURE REVIEW: DETECTION OF FINANCIAL FRAUD BASED ON MACHINE LEARNING
DOI:
https://doi.org/10.33736/uraf.9939.2025Keywords:
Machine Learning, Financial Fraud Detection, Systematic Literature Review, Financial Reports, Financial Transactions.Abstract
Deteksi kecurangan keuangan menjadi fokus penting di tengah meningkatnya ancaman penipuan digital dan kompleksitas transaksi keuangan. Penelitian ini melakukan Tinjauan Literatur Sistematis (SLR) terhadap 47 artikel ilmiah terindeks Scopus Q1 dan Q2 yang diterbitkan antara tahun 2015 dan 2025, untuk mengidentifikasi metode pembelajaran mesin , jenis kecurangan yang paling sering diteliti, dan metrik evaluasi yang digunakan. Proses SLR diterapkan sesuai dengan protokol Kitchenham dan kerangka kerja PRISMA untuk menjaga validitas dan replikasi penelitian. Hasil tinjauan menunjukkan bahwa algoritma Support Vector Machine (SVM), Artificial Neural Network (ANN), dan Logistic Regression (LR) paling umum digunakan, terutama dalam kasus penipuan kartu kredit, penipuan laporan keuangan, dan penipuan asuransi . Evaluasi model umumnya menggunakan metrik akurasi, presisi, recall, F1-score , dan AUC . Penelitian ini memberikan kontribusi penting untuk memetakan tren, pendekatan dominan, dan kesenjangan penelitian mengenai deteksi penipuan keuangan berbasis pembelajaran mesin, sehingga dapat berguna sebagai referensi strategis untuk pengembangan sistem deteksi yang lebih adaptif dan efisien di masa mendatang.
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