Financial institutions continue to invest heavily in compliance technology, spending billions each year to strengthen their ability to detect and prevent financial crime. Yet despite increasingly sophisticated systems, Chinese money laundering networks (CMLNs) remain a persistent global threat.
These networks funnel hundreds of billions of dollars through illicit channels, funding everything from drug trafficking and human exploitation to luxury goods smuggling and real estate fraud, stated Consilient in a recent post.
CMLNs have become deeply entwined with other criminal ecosystems, particularly Mexican cartels. Their operations support the trade in fentanyl and other narcotics, while also touching legitimate sectors such as healthcare, retail, and property. A “Financial Trend Analysis” by the U.S. Department of the Treasury’s Financial Crimes Enforcement Network (FinCEN) reviewed more than 137,000 Bank Secrecy Act filings between 2020 and 2024, uncovering over $300bn in suspicious activity. FinCEN has since urged banks to stay alert to such transactions and strengthen anti-money laundering (AML) vigilance.
The financial and reputational cost of non-compliance can be devastating. In one 2024 case, a major institution was fined over $3bn and faced criminal charges after failing to detect laundering tied to a single customer who processed $470m in drug proceeds. To address these failures, regulators including FinCEN have encouraged banks to explore innovative AML approaches—particularly those that use privacy-preserving AI to collaborate more effectively across institutions.
CMLNs thrive on regulatory blind spots. Because both China and Mexico restrict the movement of U.S. currency, these networks have developed complex methods to bypass capital controls. They employ informal value transfers, trade-based laundering, and “mirror” transactions, which replicate deposits and transfers simultaneously across jurisdictions. For example, U.S.-based CMLN agents may accept cartel cash in dollars, exchange it for pesos in Mexico, and then sell the dollars to Chinese buyers who fund equivalent yuan transfers in China. The funds are often laundered further through cashier’s checks, property purchases, or luxury assets.
To help financial institutions detect these schemes, FinCEN has published 18 red flags associated with CMLN transactions. These include unexplained large cash deposits, same-day foreign wire transfers, suspicious use of Chinese passports, excessive credit-card purchases of high-value goods, and unusual real estate transactions. Banks are required to file Suspicious Activity Reports (SARs) when they identify such patterns and must maintain risk-based due diligence procedures to minimise exposure to foreign agent activity.
Federated AI has emerged as a particularly promising solution for detecting such complex networks. This technology enables institutions to train machine learning models on their internal data while sharing only the model’s learned parameters—not the raw data itself. A central coordinating hub then aggregates and refines the models across multiple institutions, creating a collective intelligence system that enhances AML detection without breaching privacy or data-localisation laws.
For AML teams, federated learning allows banks to spot suspicious behaviour that may span multiple organisations, such as mule accounts or cross-institutional activity. By combining insights across distributed datasets, the model becomes better at identifying anomalies consistent with laundering patterns.
This approach also strengthens collaboration without compromising competitive boundaries. Each participating institution contributes to and benefits from a more robust global defence model while retaining full control over its sensitive data. In an era where money launderers exploit fragmented oversight, federated AI offers a means of building collective, privacy-preserving resilience across the financial system.
By implementing federated AI and other advanced technologies, financial institutions can enhance detection capabilities, mitigate compliance risks, and demonstrate to regulators that they are proactively meeting their AML obligations.
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