Inside the Crypto Laundering Machine: A Taxonomic Study of Crypto Money Laundering Schemes and Countermeasures

Abstract

The rise of Web3 and Decentralized Finance (DeFi) has enabled borderless access to financial services empowered by smart contracts and blockchain technology. However, the ecosystem’s trustless, permissionless, and borderless nature presents substantial regulatory challenges. The absence of centralized oversight and the technical complexity create fertile ground for financial crimes. Among these, money laundering is particularly concerning, as in the event of successful scams, code exploits, and market manipulations, it facilitates covert movement of illicit gains. Beyond this, there is a growing concern that cryptocurrencies can be leveraged to launder proceeds from drug trafficking, or to transfer funds linked to terrorism financing.

Crypto money laundering remains diverse and insufficiently systematized, with prior work fragmented, dated, or limited to individual vectors. We address this through a systematic, interdisciplinary review of academic, regulatory, and industry sources. We first establish the necessary background: how the classical placement, layering and integration stages of laundering adapt to Web3, the pseudonymity that underpins these schemes, and the fact that tracing cryptocurrency transactions requires access to data traces that come in varied forms. Building on this foundation, we consolidate known schemes into a taxonomy of money laundering in Web3, organizing laundering into a set of high-level obfuscation strategies and the intermediary mechanisms through which they are realized, while also identifying emerging schemes not previously documented. We systematize these intermediary mechanisms along several dimensions, including the intent behind their design and whether they are purpose-built privacy tools or exploited infrastructure, their custodial or non-custodial nature, their deployment layer, and their underlying privacy techniques.

We then turn to the countermeasures raised against these schemes, spanning both detection, which uncovers illicit activity after it occurs, and prevention, which limits the opportunity for laundering in the first place, as pursued across academia, regulation, and industry. From this synthesis we draw out the open challenges that remain, and chart future research directions toward a more transparent Web3 financial ecosystem, offering insights for researchers, policymakers, and practitioners.