Patterns in the Wash: An Empirical Analysis of Cryptocurrency Mixers in the Money Laundering Cycle
Bibliographic record
Abstract
The growth in cryptocurrency’s adoption has also expanded opportunities for criminal activity, particularly through cryptocurrency money laundering (CML). Cryptocurrency mixers play a central role in CML schemes by pooling and “mixing” laundered funds, thereby obscuring transaction trails and complicating anti-money laundering (AML) enforcement. Although mixers are disproportionately associated with criminal activity, current AML enforcement against mixers is largely responsive after laundering schemes have already been carried out and fails to adequately address the innovation and adaptability of mixer operators and users. Further, current research on how mixers are used by cybercriminals remains limited and primarily experimental. This study addresses this gap by analyzing 32 CML court cases, drawn from U.S. Department of Justice filings and other legal databases. By coding for variables including mixer type, jurisdiction, type of cryptocurrency used, transfer amounts, and the roles of CML actors, we identified notable patterns with CML mixer use. A small subset of mixers, including Tornado Cash and ChipMixer, facilitated a significant amount of laundering, with just five services linked to over $6 billion in tainted funds. Decentralized mixers transferred the majority of cryptocurrency, yet centralized mixers accounted for a greater proportion traced from illicit sources, reflecting criminals’ continued reliance on traditional, easy-to-use services. Bitcoin and Ether are the dominant cryptocurrencies of choice for launderers, although other altcoins play an ancillary role in CML schemes. This study classifies CML cases by distinguishing mixer overseers, personal users, and enablers, reflecting the diversity of actors involved in mixer CML schemes. Results indicate challenges in AML enforcement, particularly with addressing international regulatory gaps and the resilience of decentralized mixers. Effective responses must therefore incorporate proactive enforcement measures and improved international coordination, while simultaneously avoiding the erosion of legitimate financial privacy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".