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Identification Of Illegal Transaction Patterns In Payment System Data Using Ai/Ml: A Case Study On Online Gambling

Author

Listed:
  • 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

    (Bank Indonesia)

  • Rudy Marhastari

    (Bank Indonesia)

  • Nurkholisoh Ibnu Aman

    (Bank Indonesia)

  • Sintia Aurida

    (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.

Suggested Citation

  • 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 Margan, 2025. "Identification Of Illegal Transaction Patterns In Payment System Data Using Ai/Ml: A Case Study On Online Gambling," Working Papers WP/14/2025, Bank Indonesia.
    • Handle: RePEc:idn:wpaper:wp142025
    as

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    File URL: https://publication-bi.org/repec/idn/wpaper/WP142025.pdf
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    References listed on IDEAS

    as
    1. Dawei Cheng & Yao Zou & Sheng Xiang & Changjun Jiang, 2024. "Graph Neural Networks for Financial Fraud Detection: A Review," Papers 2411.05815, arXiv.org, revised Nov 2024.
    2. Melvyn Zhang & Yi Yang & Song Guo & Chris Cheok & Kim Eng Wong & Gomathinayagam Kandasami, 2018. "Online Gambling among Treatment-Seeking Patients in Singapore: A Cross-Sectional Study," IJERPH, MDPI, vol. 15(4), pages 1-12, April.
    Full references (including those not matched with items on IDEAS)

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    JEL classification:

    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • G18 - Financial Economics - - General Financial Markets - - - Government Policy and Regulation
    • K42 - Law and Economics - - Legal Procedure, the Legal System, and Illegal Behavior - - - Illegal Behavior and the Enforcement of Law

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