Blockchain tracing tools like Chainalysis Reactor help investigators untangle the financial networks behind illicit activity: fraud, theft, sanctions evasion, cybercrime, money laundering, and more. Guided by the right attributions, they can quickly determine which leads to follow and in many instances ultimately bring a case to trial.

But tracing alone is not a golden ticket. If an attribution is incorrect, investigators may draw the wrong conclusions. Instead of supporting a case, an improperly attributed wallet segment can severely undermine an investigation or even lead to wrongful enforcement action. Because of these risks, it’s important that the attributions investigators seek to build their case upon are themselves backed up by strong methodology.

U.S. courts have a framework for assessing whether proposed expert evidence is sufficiently reliable for presentation in court. This framework is known as the Daubert standard, and it helps judges distinguish inadmissible opinion from reliable and relevant evidence and expert testimony. Chainalysis was subjected to the Daubert standard in the 2024 case United States v. Sterlingov when a federal court considered a challenge to expert testimony based on Chainalysis Reactor. The court ruled in favor of its reliability.

Chainalysis is the first and only blockchain analytics provider to successfully meet the Daubert standard.

What is the Daubert Rule?

The Daubert rule, usually called the Daubert standard, is the test U.S. courts use to decide whether expert testimony is reliable enough to put in front of a jury. It comes from the 1993 Supreme Court case Daubert v. Merrell Dow Pharmaceuticals, Inc., which asks the trial judge to act as a “gatekeeper” for expert evidence.

Before that evidence can reach a jury, a court may hold a Daubert hearing, also called a Rule 702 hearing after Rule 702 of the Federal Rules of Evidence. The hearing exists to make sure expert testimony rests on sound, reliable foundations, scientific or otherwise, rather than opinion. This gatekeeping role replaced the older Frye standard (from Frye v. United States), which asked only whether a technique was generally accepted in its field.

Under Daubert, a judge evaluates expert evidence against several criteria:

• Testability: Can the theory or technique be (and has it been) tested?
• Peer review and publication: Has it been subjected to scrutiny by other experts?
• Known error rate: Is there a known or potential rate of error, and are there standards controlling the technique’s operation?
• General acceptance: Is the methodology broadly accepted within the relevant field?

Potential evidence does not need to satisfy each factor perfectly for a judge to deem it admissible, but the factors help test the quality of the methodology and reasoning offered in support of the evidence.

Just because one piece of expert testimony clears Daubert does not mean others will too. This is especially important in the field of blockchain analytics. Providers create their attributions through different processes and with different methodologies. The usefulness of their data will vary accordingly.

How Chainalysis met the bar

In United States v. Sterlingov, prosecutors alleged that Roman Sterlingov operated Bitcoin Fog, a cryptocurrency mixer used to launder tens of millions of dollars from illicit darknet activity. The defendant sought to block Chainalysis expert testimony, which triggered a Daubert review. That testimony was offered to show that Reactor could reliably attribute and cluster Bitcoin Fog and the darknet marketplaces that used it, and that those conclusions rested on transparent, testable, and auditable methods rather than a black-box algorithm.

The question before the court was not whether “blockchain analytics” as a category could be reliable or admissible. It was whether Chainalysis Reactor’s specific methodology was reliable enough to satisfy Daubert in that case. Judge Randolph Moss worked through the four factors.

Testability: The court found that Reactor’s clustering attribution methodology is transparent enough that its conclusions can be independently verified.

Peer review and publication: The court found that Reactor uses commonly accepted heuristics for analyzing and grouping spending activity. While Reactor itself had not been peer reviewed at the time of the hearing (a peer-reviewed study attested to its precision in 2025),  the co-spend heuristic in particular has long been discussed and relied on in academic literature.

Known error rate: FBI analyst Luke Scholl testified that he had not encountered false positives in his experience. The court also emphasized that Reactor is deliberately conservative, meaning it tends toward underinclusion rather than overclaiming addresses. This conservative approach minimizes the risk of false positives.

General acceptance: The court found that Chainalysis data is widely relied upon across law enforcement, regulators, exchanges, financial institutions, and compliance teams. It also credited evidence that Chainalysis in particular is viewed as an industry-standard tool, including across multiple U.S. government agencies and major exchanges that use Chainalysis products built on the same underlying data for anti-money laundering and transaction-monitoring purposes. It was also emphasized that the government’s case used other data sources and techniques – such as traditional forensic traces, IP logs, confession, forum posts – in addition to Chainalysis to support their case.

The judge held that Chainalysis’ blockchain analytics was reliable and admissible as substantive evidence in the Sterlingov case. Just as important, the ruling rejected the defense’s claim that Reactor was a black-box algorithm, finding instead that the methodology was transparent, tested, reviewed, and reliable.

Overall, the Bitcoin Fog decision demonstrates clear affirmation that blockchain analytics backed by scientific standards, when properly explained and corroborated, can withstand rigorous evidentiary scrutiny. The court’s acceptance of Chainalysis Reactor under Daubert demonstrates that clustering heuristics, address attribution, and forensic tracing on public ledgers can meet the standards of scientific reliability, especially when paired with independent verification and transparent expert testimony.

However, while the Sterlingov ruling validated Chainalysis Reactor’s methodology, it does not validate every blockchain analytics provider. Only Chainalysis’ methodology has passed the strict scrutiny of admissibility.

Chainalysis recently published a formal ontology for blockchain attribution that breaks down how our clusters are built and what standards each component must meet. The structural soundness standard at its core — deterministic, reproducible, and built with documented safeguards — was designed to survive exactly the kind of scrutiny Daubert demands.

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