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Blockchain analysis follows recorded cryptocurrency transfers from address to address. It can reveal transaction paths and, with additional evidence, help link addresses to a service or other real-world entity. But a public ledger does not automatically disclose who controls an address: tracing a flow, grouping addresses, and identifying a person are separate steps with different levels of certainty.

What blockchain analysis can—and cannot—show

A public blockchain records transactions, including the addresses involved and the movement of assets. Analysts can use those records to reconstruct flows: where funds came from, where they went next, and how transactions connect across a ledger. Analytics services collect and organize this information, which can include addresses, values, timestamps, smart-contract interactions, and links between addresses. The available details vary by blockchain and by the question being investigated.

The ledger itself generally identifies addresses, not the people behind them. To move from a visible transaction to a claim about a person or organization, investigators need additional evidence. A published service address, open-source information, exchange records, law-enforcement material, or a provider’s intelligence may help supply that connection. Each source supports a different kind of claim, and its reliability and currency need to be assessed.

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It helps to keep three terms distinct:

  • Trace: follow recorded funds or transaction relationships through the ledger.
  • Cluster: group addresses that a method suggests may share control.
  • Attribute: connect an address or cluster to a service or real-world entity using evidence beyond the transaction pattern alone.

A trace is not, by itself, proof of identity or wrongdoing. A cluster is an analytical inference, not a verified person. An attribution is only as strong as the evidence and method supporting it.

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How analysts trace cryptocurrency transactions

In practice, blockchain analysis is a sequence of data collection, transaction-path analysis, address grouping, attribution, and review. Some steps can be automated to make large transaction histories manageable, but conclusions still depend on method, context, and supporting evidence.

1. Collect relevant ledger data

An analyst begins with the blockchain records relevant to the case: for example, a known address, a transaction identifier, or an interval of activity. Analytics platforms organize public ledger information so that a reviewer can examine transaction history and relationships without manually searching each record. What can be observed depends on the network and the type of activity under review.

2. Follow transactions through the graph

Each recorded transfer creates a relationship between transaction participants. Starting from a known address or transaction, an analyst can follow incoming and outgoing flows to see how funds move through subsequent transactions. This produces a transaction graph: the recorded links are visible, while the identity or purpose behind them may not be.

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Transaction structure matters. In Bitcoin-style transactions, for example, an analyst may examine whether an output appears to be a change address—an address receiving the unspent remainder of a transaction. The U.S. Department of Justice’s May 2021 discussion describes identifying a possible change address and using clustering to infer common control. “Possible” matters: the pattern can support an inference, but does not turn an address into a confirmed identity.

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3. Group addresses that may share control

Some address-grouping methods rely on transaction patterns. Common-input ownership is one example used in Bitcoin-style analysis: addresses appearing as inputs in the same transaction may be treated as potentially controlled together. Change-address analysis is another. These are heuristics, meaning they provide clues rather than certainty.

Transaction context is important when evaluating a heuristic. A grouping method can make mistakes, and the conclusion that a set of addresses may share control is not equivalent to identifying the operator of those addresses. Chainalysis’s framework for blockchain address analysis explicitly treats address grouping, entity attribution, and operator determination as separate analytical operations with distinct error characteristics. Keeping them separate makes it easier to see what the evidence actually establishes.

4. Add attribution evidence

To associate an address or cluster with a service or entity, an analyst may use evidence outside the transaction pattern. Examples include a service’s published deposit address, open-source material, exchange information, law-enforcement records, or research compiled by an analytics provider. These sources differ in provenance and strength; a label in a database should not be mistaken for information written into the blockchain.

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If funds reach a regulated exchange, investigators may have an avenue to seek off-chain customer information. Whether they can obtain it depends on applicable rules and legal process. The ledger can show a transfer to an address associated with an exchange; access to customer records is a separate investigative step.

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5. Review the path and document the limits

Analytics platforms can visualize transaction paths, identify possible service or cash-out points, and flag indicators for further review. Elliptic describes risk scoring as a way to prioritize review; a score or alert is not a verdict that every transaction is suspicious. A defensible account should explain its starting evidence, the grouping and attribution methods used, the chains and period covered, and the confidence limits.

That documentation matters because structural and identity claims rely on different evidence. A transaction path may be reproducible from ledger data, while a label linking a cluster to an entity may depend on intelligence or investigative records. A reader should be able to tell which part of a conclusion comes from the public record and which part comes from outside attribution.

Why a public transaction trail does not reveal a person by itself

Blockchain addresses are pseudonymous identifiers in the record, not names. Seeing funds move between addresses establishes a transaction history, but not necessarily who controlled each address, why a transfer happened, or whether the same person controlled multiple addresses. Those conclusions require additional attribution evidence and an explanation of how it was connected to the ledger activity.

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Even when an analytics provider labels an address or cluster, that label can be incomplete, mistaken, or out of date. Chainalysis warns about false positives and stale attribution in its material. A label can be useful as an investigative lead, but its source, date, confidence, and scope matter. A service attribution, for example, does not automatically identify the individual who initiated a particular transaction.

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For anyone reviewing a tracing claim, the key question is not simply whether a graph exists. Ask what the graph proves from the ledger, what conclusions depend on heuristics, and what outside evidence supports the identity label. Those are separate evidentiary layers, not interchangeable descriptions of the same fact.

How mixing and other obfuscation affect tracing

Obfuscation can disrupt or complicate the links an analyst is trying to follow. The DOJ’s May 2021 article discusses mixers or tumblers, chain hopping, and peel chains as techniques that can challenge blockchain analysis. It says mixing can make tracing highly impractical in some circumstances and notes that chain hopping involving privacy-enhanced cryptocurrencies can increase difficulty.

That is a description of investigative challenges, not a rule that every use of a mixer defeats analysis—or that every mixed flow can be reconstructed. Outcomes depend on the specific activity, the networks involved, the available data, and the evidence investigators can obtain. The DOJ article also notes that clustering, off-chain data scraping, tracing, and other techniques can be foiled by cryptocurrency laundering methods. Because that discussion dates to May 2021, it should not be read as a complete account of every present-day service or investigation.

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Cross-chain activity adds another complication. Elliptic describes cross-chain visibility as a capability of its service, but a provider’s capability claim does not establish that every bridge, swap, or privacy-enhanced path can be reconstructed. When a tracing report spans chains, ask which networks and transaction types were actually covered and what happens to the analysis where visibility is incomplete.

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What measured tracing accuracy does—and does not—mean

A 2025 USENIX Security study by Kelvin Lubbertsen, Michel van Eeten, and Rolf van Wegberg, “Ghost Clusters: Evaluating Attribution of Illicit Services through Cryptocurrency Tracing,” evaluated Chainalysis attribution against ground-truth data for three seized services: BestMixer, Hansa Market, and Wall Street Market.

For those cases, the study reported accuracy lower bounds ranging from 24.54% to 94.85%, fewer than 0.5% false positives in the studied sample, and coverage that changed over time. The wide range is important: it shows that measured results varied across the evaluated services and periods. These figures are not a general accuracy rate for blockchain analytics, not a comparison of all providers, and not a promise about the result in any particular investigation.

Accuracy and coverage answer different questions. A method may correctly identify some of the addresses it attributes while missing others; the number of addresses covered can also change over time. The study’s results therefore support a cautious conclusion: attribution tools can provide useful investigative information, but performance must be understood in relation to the cases, ground truth, and time period examined.

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How to assess a tracing tool or a tracing claim

Whether you are evaluating a report, a software capability, or an investigative conclusion, ask for the scope and evidence behind it rather than relying on a visually persuasive graph or a single risk label.

What to check Why it matters
Chains and transaction data in scope Coverage varies by blockchain and by the question being investigated. A claim about activity on one chain does not establish visibility into every chain involved.
Address-grouping method Find out what heuristic supports a cluster and whether the conclusion is presented as a possibility or a verified control relationship.
Source and age of an entity label A service or entity attribution comes from outside evidence, not simply from the transaction record. Its provenance and currency affect how much weight it deserves.
Cross-chain handling Ask which cross-chain activity is covered and where the analyst’s visibility ends; a general capability description does not establish that every path is traceable.
Confidence, limitations, and supporting evidence A useful report makes uncertainty reviewable and distinguishes reproducible ledger observations from intelligence-based attribution.

Legal use adds another requirement: methods must be explainable and defensible. 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 that cannot be meaningfully examined is harder to evaluate, regardless of how clear its visualization appears.

The practical takeaway

Blockchain analysis is strongest at showing recorded transaction relationships. Address clustering can extend that view by suggesting common control, and outside evidence can support attribution to a service or entity. Those steps are valuable but not interchangeable: a visible flow is not a name, a cluster is not a person, and a risk score is not proof of wrongdoing. The most reliable reading of a tracing claim keeps each step—and its uncertainty—visible.

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