Chinese-language crypto laundering networks processed ~$16.1B in 2025, or $44M per day on average across 1,799+ wallets, representing 20% of laundering activity
TL;DR — After emerging at the start of the pandemic, Chinese-language money laundering networks (CMLNs) …
Context & Ripple Effects
Chainalysis’ earlier coverage showed crypto laundering volumes moving from $8.6B in 2021 to nearly $23.8B in 2022, before a reported 29.5% decline to $22.2B in 2023. The new finding isolates a Chinese-language network segment that now represents a material share of the activity rather than treating laundering as a single undifferentiated flow.
The history also shows recurring concentration: in 2020, five illicit fund-receiving services accounted for 55% of laundering through just 270 deposit addresses, a pattern that makes the newer identified high-value laundering addresses especially consequential for tracing and disruption.
First-order effects
- The 1,799+ wallets identified as part of these networks become a more actionable target set for exchanges, stablecoin issuers, and blockchain-monitoring providers screening deposits, withdrawals, and counterparties.
- The report gives compliance teams a basis to prioritize network-level exposure over isolated suspicious wallets, because the named segment processed about one-fifth of reported laundering activity.
Second-order effects
- Platforms and payment services connected to the identified wallet clusters may face tighter due diligence and more transaction holds, while analytics vendors gain demand for attribution and cross-wallet tracing.
- The finding reinforces a shift away from focusing solely on a small number of endpoints: earlier reporting found five services dominated 2020 laundering inflows, while this report describes a much broader wallet footprint.
Third-order effects
- If network-based laundering continues to account for a large share of crypto illicit flows, compliance competition will increasingly center on identifying organizations, communications channels, and off-ramp relationships—not just flagging individual addresses.
- The result could deepen the crypto legitimacy gap: institutions may impose broader risk controls on opaque transaction networks unless investigators and platforms can distinguish illicit clusters from legitimate users with similar on-chain behavior.
The trend: Crypto anti-money-laundering enforcement is moving from tracking conspicuous receiving addresses toward mapping resilient, cross-wallet financial networks.