Money Laundering Typology Detection Using Graph Analytics and Neural Networks

Lalu Garin Alham; Nadia Tsabitah; Yusuf Muhammad Nur Zaman

doi:10.59593/amlcft.2025.v4i1.269

Penulis

Lalu Garin Alham Espay Debit Indonesia Koe (DANA)
Nadia Tsabitah Bank Rakyat Indonesia
Yusuf Muhammad Nur Zaman Bank Rakyat Indonesia

DOI:

https://doi.org/10.59593/amlcft.2025.v4i1.269

Kata Kunci:

Kejahatan keuangan, Pembelajaran mesin, Pencucian uang, Teori graf

Abstrak

Pencucian uang diestimasi mencakup 2–5% dari Produk Domestik Bruto (PDB) global setiap tahunnya, dengan skala yang terus meningkat seiring ekspansi ekosistem digital. Sistem Anti-Pencucian Uang (AML) konvensional yang mengandalkan aturan (rule-based) dan pola transaksi memiliki keterbatasan dalam mendeteksi perilaku relasional pada kejahatan keuangan. Penelitian ini mengusulkan kerangka analitik graf terintegrasi untuk mendeteksi pola struktural pencucian uang dengan memanfaatkan metrik graf yang diintegrasikan ke dalam alur kerja (pipeline) jaringan saraf tiruan. Studi ini mengevaluasi metrik eksentrisitas, derajat (degree), kedekatan (closeness), serta arah aliran dana untuk membedakan aktivitas pencucian uang. Hasil uji Welch’s t-test mengonfirmasi adanya perbedaan signifikan secara statistik pada lima dari enam metrik yang diuji (p<0,001). Selanjutnya, model Multi-Layer Perceptron (MLP) diterapkan untuk mengklasifikasikan 17 tipologi pencucian uang dengan tingkat akurasi mencapai 80%. Kontribusi utama penelitian ini membuktikan bahwa tipologi kejahatan keuangan dapat diekstraksi langsung dari topologi jaringan tanpa bergantung sepenuhnya pada fitur transaksi. Dengan menghubungkan metrik graf terhadap perilaku pencucian uang, termasuk pola placement, layering, dan integration. Penelitian ini menawarkan pendekatan deteksi AML berbasis jaringan yang skalabel. Penelitian mendatang disarankan untuk berfokus pada validasi data riil serta pengembangan alur klasifikasi waktu nyata (real-time) menggunakan inferensi jaringan saraf berbasis graf.

Unduhan

Data unduhan belum tersedia.

Biografi Penulis

Lalu Garin Alham, Espay Debit Indonesia Koe (DANA)

Fraud Investigation Senior Manager

Nadia Tsabitah, Bank Rakyat Indonesia

Junor Manager Data Science for Audit Specialist

Yusuf Muhammad Nur Zaman, Bank Rakyat Indonesia

Data Science for Audit Specialist

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