STAB-AD: Framework Deteksi Anomali Mobile Banking Berbasis Perilaku Dan Spatio-Temporal
DOI:
https://doi.org/10.30865/json.v7i4.9685Keywords:
STAB-AD, Deteksi Anomali, Mobile Banking , Spatio-Temporal, FraudAbstract
Perkembangan mobile banking meningkatkan risiko fraud, seperti unauthorized transaction dan account takeover (ATO), yang semakin kompleks dan sulit dideteksi. Penelitian ini mengusulkan metode Spatio-Temporal and Behavioral Agreement-based Anomaly Detection (STAB-AD) untuk mendeteksi anomali transaksi melalui integrasi fitur perilaku dan spatio-temporal. Model mengombinasikan Isolation Forest, Local Outlier Factor, dan One-Class SVM dengan mekanisme agreement-based untuk mengukur konsistensi antar model dan mengklasifikasikan tingkat risiko. Fitur yang digunakan mencakup frekuensi transaksi, deviasi nilai, time gap, perubahan perangkat, serta indikator spatio-temporal seperti jarak geografis, velocity, dan impossible travel. Evaluasi dilakukan menggunakan precision, recall, F1-score, dan ROC-AUC dengan pendekatan pseudo-labeling berbasis aturan sebagai proksi ground truth pada data tidak berlabel. Hasil menunjukkan bahwa STAB-AD mampu meningkatkan kinerja deteksi dibandingkan baseline serta efektif dalam risk stratification, dengan hanya 0,72% transaksi teridentifikasi sebagai risiko tinggi. Fitur spatio-temporal terbukti signifikan dalam mengidentifikasi pola perpindahan tidak wajar yang mengindikasikan potensi ATO. Namun, penelitian ini masih terbatas pada penggunaan threshold berbasis asumsi dan validasi tanpa label aktual, sehingga diperlukan pendekatan yang lebih data-driven serta evaluasi berbasis ground truth pada penelitian selanjutnya.
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