
Between 16 March and 15 April 2026, the Singapore Police Force executed a targeted anti-scam operation that did not rely on post-incident recovery, but on interrupting transaction flows while they were still active.
Working directly with exchanges including Coinbase, Gemini, Upbit, Coinhako, Independent Reserve and StraitsX, investigators embedded blockchain tracing into live transaction monitoring. Using analytics infrastructure from Chainalysis and TRM Labs, authorities mapped wallet interactions linked to ongoing scams and traced fund movements across platforms in near real time.
The operation focused on identifying victims before full capital deployment. In most cases, scams followed a staged pattern—initial deposits were kept small to build confidence, followed by larger transfers once fabricated returns were displayed. By tracking these early-stage flows, law enforcement was able to intervene during the escalation phase, contacting more than 90 individuals before additional funds were committed.
A key element was exchange-side visibility. Once flagged wallet clusters were linked to user accounts, platforms were able to support rapid identification and enable intervention before assets moved off-platform or into further obfuscation layers. This timing proved critical, as recovery rates drop sharply once funds pass through multiple wallets or leave regulated venues.
The operation prevented an estimated $2.86 million in losses, but more notably demonstrated a shift in execution model. Detection was driven by transaction behaviour rather than complaints, and response depended on coordinated access to exchange-level data rather than isolated investigations.
The approach consolidates three layers—on-chain analytics, platform cooperation, and direct intervention—into a single workflow. In practice, this reduces the gap between suspicious activity and enforcement action from days to hours, particularly in cases where funds remain داخل exchange environments.
The Singapore exercise highlights a move toward operational integration, where fraud control is embedded within transaction infrastructure rather than applied after funds are lost.