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What blockchain analysis can—and cannot—show
A public blockchain is a ledger of transactions. Depending on the network, its records can expose addresses, transaction amounts, timestamps, smart-contract interactions, and links between transactions. An analyst can use those records to follow a flow of funds through the ledger. The visible record is not, by itself, a list of account holders or legal names.
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That distinction separates three claims that are easy to blur:
| Claim | What it means | What supports it |
|---|---|---|
| Trace | Funds moved through a sequence of recorded transactions or addresses. | Ledger data showing the transactions and their connections. |
| Cluster | Several addresses may be controlled by the same entity. | Patterns in transaction structure, interpreted as heuristics rather than proof of common ownership. |
| Attribute | An address or cluster may belong to a named service, organization, or person. | Additional intelligence, such as published service addresses, exchange information, open-source material, or investigative records. |
The distinction matters in investigations, compliance reviews, and news reports. A trace is a statement about recorded movement. A cluster is an inference about common control. An attribution connects an address or group to an entity using evidence beyond the movement itself. A risk label or graph visualization can help identify a lead, but it does not turn an inference into a finding of wrongdoing.
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Chainalysis’s framework for blockchain address analysis treats address grouping, entity attribution, and determining an operator as separate analytical operations, each with its own evidence requirements and possible errors. That is a useful way to read any tracing claim: ask which of these steps has actually been established, and what evidence supports it.
How analysts trace a transaction
Tracing is a sequence of steps, not a single lookup. The exact process depends on the blockchain, the starting evidence, the time period, and the question being investigated. Analytics platforms can aggregate ledger histories and visualize connections, but an investigator still has to interpret what the data does and does not establish.
- Start with a known transaction or address. The starting point might be an address already associated with a reported incident, a transaction identified in an investigative record, or an address published by a service. The origin of that starting point matters: a ledger can confirm activity from an address, but it cannot validate who first identified the address or why it is relevant.
- Collect the relevant ledger history. Analysts gather transactions and associated details, such as values, timestamps, and, where applicable, smart-contract interactions. The available details and how they are represented differ among blockchains. The scope should be clear: which network, addresses, and dates are included.
- Follow the transaction connections. The analyst follows incoming and outgoing flows from the starting point, mapping how value moves through subsequent transactions. This can reveal paths, possible service touchpoints, and points where the trail becomes harder to interpret. A visual path is a map of ledger relationships, not an identification of every controller along it.
- Assess whether addresses may share control. Transaction patterns can support a heuristic grouping of addresses. Analysts must consider the network’s transaction structure and the context around a pattern; a grouping is an inference, not a directly recorded ownership fact.
- Seek attribution evidence. A separate body of information may connect an address or cluster to a service or person. Examples include a service’s published deposit address, open-source material, exchange information, or law-enforcement records. The source and freshness of that information affect how much confidence it warrants.
- Document the reasoning and uncertainty. A defensible account identifies the starting evidence, the networks and time period examined, the grouping method, the source of any labels, and the confidence limits. It should make clear which conclusions follow directly from ledger data and which depend on outside intelligence.
Automation helps investigators handle transaction histories at a scale that would be difficult to review manually. It does not remove the need to interpret patterns, verify labels, or explain the analytical choices behind a conclusion. The U.S. Department of Justice’s May 2021 discussion of blockchain analysis describes investigators using transaction structure, including possible Bitcoin change addresses, alongside clustering to infer common control. The word “possible” is important: an apparent change address is an analytical judgment, not a label embedded in the blockchain.
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Why address clustering is an inference
Blockchains generally record transactions and addresses, not a universal identity for the person controlling each address. To make analysis more useful, analysts may group addresses that appear to be operated together. On Bitcoin-style transactions, common-input ownership and change-address patterns are examples of heuristics used to suggest that addresses share control.
A heuristic is a clue derived from a pattern; it is not a guarantee. A clustering method can group addresses that do not share an operator, or miss addresses that do. Its reliability depends on the transaction context and the assumptions behind the method. The grouping should therefore be reported as a cluster supported by specified evidence—not quietly upgraded to a verified individual or organization.
Clustering also does not answer who an operator is. A group of addresses may be associated with a service through separate evidence, but that attribution is another claim and needs its own support. Chainalysis’s formal-ontology discussion emphasizes this separation and notes that analytical operations have distinct error characteristics. In practical terms, confidence in a grouping does not automatically transfer to confidence in the identity attached to it.
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How an address gets linked to a real-world entity
Attribution usually depends on information outside the bare transaction trail. Analysts may find a service’s own published address, connect activity to open-source material, use information supplied by an exchange, or rely on records gathered in an investigation. An analytics provider may also attach labels based on its own research. Those labels can make a graph easier to navigate, but readers should ask how the label was sourced, when it was last checked, and whether it identifies a service, a cluster, or a particular operator.
When funds reach a regulated exchange, investigators may have an avenue to seek off-chain customer information, subject to applicable rules and legal process. The ledger can help identify a transaction path to an exchange-associated address; it does not itself disclose the exchange’s customer records or establish what those records contain. Access to, and interpretation of, off-chain evidence are separate from tracing the on-chain flow.
These evidence sources can reinforce one another, but they should not be collapsed into one claim. A useful explanation distinguishes what the ledger records, what a provider’s intelligence database asserts, and what an outside investigation has established. Elliptic’s explainer describes blockchain analytics capabilities including risk indicators and cross-chain visibility; those are provider descriptions of its service, not proof that every address or cross-chain path can be identified.
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Why some transaction paths are harder to follow
Obfuscation techniques can disrupt, complicate, or lengthen a tracing path. In a May 2021 article, the U.S. Department of Justice discussed mixers or tumblers, chain hopping, and peel chains as techniques used in cryptocurrency money laundering. It said that mixing can make tracing highly impractical in some circumstances, and that chain hopping involving privacy-enhanced cryptocurrencies can increase difficulty. This is a dated government discussion, not a statement that every present-day service works the same way or that every investigation faces the same limits.
Obfuscation does not support a universal conclusion in either direction. It would be too strong to say that every mixed flow becomes impossible to follow, just as it would be too strong to say that every mixed flow can be reconstructed. The result depends on the particular path, the available ledger and off-chain evidence, and the methods an investigator can lawfully and reliably apply. The DOJ article’s broader point is that techniques including clustering, off-chain data scraping, and tracing can be foiled by money-laundering methods; the practical effect varies by case.
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Cross-chain activity poses a related challenge. Moving value across networks can require an analyst to understand the records and transaction structures on more than one chain. A provider’s claim of cross-chain visibility should not be read as a guarantee that every bridge, swap, or privacy-enhanced route can be reconstructed. The scope of the supported chains and the treatment of the particular path are questions to ask before relying on a cross-chain graph.
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What measured performance does—and does not—tell you
There is no single accuracy percentage that describes blockchain tracing in every case. Results depend on the provider, the method, the type of service being analyzed, the available ground truth, and the period covered. A study with a defined sample can show how a method performed under those conditions, but it cannot establish a universal error rate for other providers or investigations.
In a 2025 USENIX Security Symposium study, Kelvin Lubbertsen, Michel van Eeten, and Rolf van Wegberg evaluated Chainalysis attribution against ground-truth data for three seized illicit services: BestMixer, Hansa Market, and Wall Street Market. They reported accuracy lower bounds ranging from 24.54% to 94.85%, fewer than 0.5% false positives in that evaluation, and coverage that changed over time. The wide range and time variation are as important as the best result: attribution performance differed across the studied cases and was not static.
Those figures describe that study’s evaluation, sample, and ground-truth comparisons. They do not mean that any particular tracing tool is between 24.54% and 94.85% accurate in general, nor that another case will have fewer than 0.5% false positives. A low false-positive result in a bounded sample also does not answer how many relevant addresses were missed; coverage is a separate consideration.
Risk scores have a similarly limited role. Elliptic describes risk scoring as a way to prioritize review, rather than treating every transaction with an indicator as suspicious. A score or flag can help an analyst decide what to examine next; it is not, by itself, evidence that a transaction was criminal or that an address belongs to a particular person.
How to evaluate a tracing claim or tool
Whether reviewing a report, a compliance alert, or a provider’s analytics, ask for enough detail to distinguish recorded facts from inferences. The following checks focus on the evidence behind a conclusion rather than the visual polish of a graph.
- Scope: Which blockchain networks, transaction data, addresses, and dates are covered? A conclusion about one chain or period should not be presented as though it covers all activity.
- Grouping: What heuristic supports treating addresses as one cluster? Is the method described, and is the transaction context considered?
- Attribution: What is the source of each entity label? Is it based on published service information, provider research, exchange information, or investigative records, and how current is it?
- Cross-chain handling: If the analysis crosses networks, does it explain how the connection was established and which parts remain uncertain?
- Confidence and error: Are confidence limits and possible false positives disclosed? Does the report distinguish coverage from accuracy?
- Reviewability: Can a human reviewer inspect the supporting evidence and understand the method, rather than having to accept an unexplained label or proprietary output?
These questions are especially important when a conclusion may have legal or financial consequences. The DOJ’s May 2021 article notes practical and legal challenges in admitting blockchain-analysis evidence, including concerns about proprietary methods that are difficult to explain in court. A conclusion is more useful when another reviewer can see how it was reached and what would weaken it.
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