Money Laundering In Cryptocurrency: Trends And Detection Techniques

Money laundering in the cryptocurrency ecosystem is evolving, encompassing a wide range of illicit activities. According to Chainalysis, the latest research delves into the complexities of crypto-based money laundering and introduces advanced data techniques for detection and investigation.

Expanding Crypto's Role in Money Laundering

Public blockchains, while transparent and traceable, are increasingly used by illicit actors to launder funds due to their cross-border, instantaneous, and cost-effective nature. This trend is not limited to cybercriminals; it now includes broader illicit activities such as narcotics trafficking and fraud. The ubiquity of cryptocurrency has made it a tool for laundering proceeds from various off-chain crimes.

This shift has significant implications for investigators. Expertise in cryptocurrency must extend beyond specialized cybercrime units to include all law enforcement agencies. Cryptocurrency is now one of the payment methods used by illicit actors worldwide, necessitating a comprehensive understanding of both blockchain transaction tracing and traditional money laundering tactics. However, the transparency of blockchain provides a silver lining: with the right data and tools, investigators can uncover illicit activity that might otherwise go undetected.

Understanding Money Laundering

Money laundering involves concealing the origins of money obtained from illegal activities, making the funds appear legitimate. The process generally consists of three stages: placement, layering, and integration. Placement introduces illicit money into the financial system, layering obscures its origin through a series of transactions, and integration re-enters the money into the legitimate economy.

Chainalysis has published analyses in its annual Crypto Crime Reports, dissecting the flow of funds from known illicit wallets. These wallets hold funds connected to confirmed crypto-native criminal activity, such as exchange heists, scams, and darknet market proceeds. Conversion services, which include centralized exchanges, DeFi services, gambling sites, mixers, and bridges, represent the layering stage of laundering. Because this activity occurs entirely on-chain, it can be traced and analyzed with a higher degree of accuracy and speed compared to traditional financial systems.

Significant Findings

Since 2019, nearly $100 billion in funds have been sent from known illicit wallets to conversion services. The highest amount recorded was in 2022, with $30 billion identified, largely attributable to transactions involving sanctioned services such as the Russian exchange Garantex.

These amounts represent the dollar value of the assets at the time they leave wallets associated with illicit actors. These estimates only include the totals moved from illicit sources to crypto services and do not include the value sent and received among intermediaries, which can involve numerous transactions. This estimate also excludes transactions where cryptocurrency is used to launder funds, but the source of the illicit activity is unidentified or off-chain.

For example, a drug cartel selling narcotics and paying a distributor using cryptocurrency might have transactions flowing directly between two known exchanges. Without specific lead information, this would be indistinguishable on-chain from legitimate service-to-service transfers. However, investigators can still follow these funds using a combination of off-chain intelligence and on-chain analysis, and compliance teams can flag unusual transactions outside of their customers' business profiles.

Conclusion

This report broadens the analysis of money laundering to include not only crypto-native activities but also suspicious transaction patterns that may indicate money laundering tied to off-chain crimes. The ongoing research underscores the importance of advanced blockchain intelligence and data-driven insights in combating financial crimes.

For more information, visit Chainalysis.

• cryptocurrency
• money laundering
• blockchain analysis