IDENTIFICATION OF ILLEGAL TRANSACTION PATTERNS IN PAYMENT SYSTEM DATA USING AI/ML: A CASE STUDY ON ONLINE GAMBLING

Renardi Ardiya Bimantoro, Rudy Hardiyanto, Irfan Sampe, Agung Bayu Purwoko, Imam Dwi Kuncoro, Irvan Fadjar R., Devima Christi M., Anugerah Mohamad Setiawan, Moh. Mashudi Arif, Mahanani Margani, Dwi Kartika Siregar, Ganang Suryo Anggoro, Melati Pramudyastuti, Farah Hilda Fuad Lubis Hilda Fuad Lubis, Rudy Marhastari, Nurkholisoh Aman and Sintia Aurida
Additional contact information
Renardi Ardiya Bimantoro: Bank Indonesia
Rudy Hardiyanto: Bank Indonesia
Irfan Sampe: Bank Indonesia
Agung Bayu Purwoko: Bank Indonesia
Imam Dwi Kuncoro: Bank Indonesia
Irvan Fadjar R.: Bank Indonesia
Devima Christi M.: Bank Indonesia
Anugerah Mohamad Setiawan: Bank Indonesia
Moh. Mashudi Arif: Bank Indonesia
Mahanani Margani: Bank Indonesia
Dwi Kartika Siregar: Bank Indonesia
Ganang Suryo Anggoro: Bank Indonesia
Melati Pramudyastuti: Bank Indonesia
Farah Hilda Fuad Lubis Hilda Fuad Lubis: Bank Indonesia
Sintia Aurida: Bank Indonesia

No WP/14/2025, Working Papers from Bank Indonesia

Abstract: The transformation of Indonesia’s payment system, driven by BSPI initiatives such as SNAP, QRIS, and BI-FAST, has made digital payments faster, more affordable, and more accessible. However, these advancements can also be misused for illegal activities, specifically online gambling. With transactions projected to grow rapidly from Rp327 trillion in 2023 to Rp900 trillion in 2024, this issue has become a major national financial concern. Beyond eroding public trust, this poses serious social and legal risks. Standard monitoring simply cannot keep up with these shifting threats. To address this, this study proposes an AI-driven Fraud Detection System (FDS). By using a hybrid machine learning approach, combining clustering, classification, and GraphML, we can map out criminal networks and how accounts interconnect. The results indicate that the system identified over 90% of syndicate accounts linked to gamblers. It also cut the time required to flag 1,000 fraudulent accounts from a week of manual work down to just 30 minutes, while catching three times the volume of fraud. These insights offer a strong basis for creating adaptive, risk-based policies that reinforce the integrity and resilience of Indonesia's payment ecosystem.

Keywords: AI/Machine Learning; Judi Daring; Sistem Pembayaran; Bank Indonesia; Pengawasan Keuangan; Deteksi Penipuan (search for similar items in EconPapers)
JEL-codes: C55 G18 K42 (search for similar items in EconPapers)
Pages: 29 pages
Date: 2025
New Economics Papers: this item is included in nep-cmp, nep-law and nep-pay
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