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.