Entity Resolution on Blockchain: Linking Wallets to Real-World Taxpayers

Blockchain was designed for pseudonymity, not identity. Transactions are fully transparent, but the individuals behind them are not directly visible. Yet today, billions of dollars in crypto transactions move through systems where activity is visible, but the actors are not.

For tax authorities, this creates a paradox. There is unprecedented data visibility, but limited accountability. Analysis from the Organization for Economic Co-operation and Development suggests that a meaningful share of crypto users either underreport or fail to report digital asset activity, prompting the development of global frameworks such as the Crypto-Asset Reporting Framework (CARF). At the same time, central bank surveys from the European Central Bank indicate that between 6–10% of households in 6 advanced Euro economies have engaged with crypto-assets, underscoring the growing scale of adoption. The gap is no longer about access to data; it is about linking that data to real-world taxpayers. This is where entity resolution on blockchain becomes critical.

Entity resolution is the process of linking blockchain wallet activity to real-world individuals or organizations. In traditional financial systems, identity is embedded through KYC frameworks and regulated intermediaries. In contrast, blockchain ecosystems operate with pseudonymous identifiers, where identity exists outside the system and must be reconstructed.

This distinction fundamentally changes how compliance must be approached. Instead of relying on embedded identity, authorities must piece together signals across datasets, behaviors, and transaction flows. Bridging this divide is essential for identifying taxable events, detecting evasion, enforcing compliance frameworks, and building audit-ready evidence. Without entity resolution, blockchain transparency has limited regulatory value.

The first step in entity resolution is wallet clustering, which involves grouping addresses that likely belong to the same entity. This is typically achieved using heuristics such as common input ownership, change address detection, and transaction graph analysis.

Over time, these techniques reveal patterns of control, showing how assets move under a single entity’s influence. At scale, clustering creates a map of economic activity across the blockchain, highlighting relationships between wallets, services, and counterparties. However, clustering alone does not establish identity. It answers what is connected, but not who is behind it. For regulators, this means clustering is necessary, but insufficient on its own.

Behavioral analytics builds on clustering by adding context to transaction activity. By analyzing patterns such as frequency, timing, counterparties, transaction size distribution, and interactions with exchanges or DeFi protocols, analysts can infer the nature of the entity behind the activity.

For example, a retail investor may exhibit periodic accumulation and long holding periods, while a trading entity may show continuous movement across exchanges and liquidity pools. Institutional actors often demonstrate structured, high-volume flows, while illicit actors may rely on layering strategies, rapid transfers, and interaction with obfuscation services.

Research from the Bank for International Settlements highlights that global crypto participation surged significantly between 2020 and 2022, with a large share of activity driven by retail investors. These behavioral patterns provide additional context for distinguishing between user types and prioritizing investigative focus.

This layer is critical because it transforms raw blockchain data into intelligence. Instead of simply observing transactions, regulators can begin to understand intent, classify activity, and identify anomalies that warrant deeper investigation.

True attribution is achieved when on-chain data is connected with off-chain information. This includes exchange KYC records, government databases, financial institution data, sanctions lists, and real-world asset registries.

This step is what converts probabilistic insights into legally defensible identity. For instance, when a clustered set of wallets interacts with a regulated exchange, the associated KYC data can serve as a bridge between blockchain activity and a verified individual or entity. Similarly, linking transaction patterns to financial flows or declared income can reveal discrepancies relevant for tax enforcement.

Without external data linkage, blockchain analytics remains an approximation. With it, authorities can establish clear, actionable connections between wallet activity and taxpayers, enabling enforcement actions, audits, and compliance monitoring.

Digital asset transactions rarely follow simple, linear paths. Funds often move across multiple wallets, chains, and intermediaries, including mixers, bridges, and decentralized protocols. These movements are often designed to obscure origin and ownership.

Multi-hop transaction analysis enables investigators to trace these flows across complex networks. By following transactions across hops, identifying convergence points, and mapping relationships between entities, authorities can reconstruct the full lifecycle of funds.

This is particularly relevant in a global financial system where cross-border value flows are significant. According to the World Bank, remittance flows alone exceed $800 billion annually, and digital assets are increasingly being explored as alternative rails for transferring value. As crypto adoption intersects with these flows, the need for robust tracing capabilities becomes even more critical.

At scale, this capability requires not only advanced analytics but also high-performance processing to handle large volumes of data in near real time. Without it, critical connections may be missed, and investigative timelines can extend significantly.

Despite the transparency of blockchain systems, several challenges complicate attribution. Pseudonymity remains a core feature, and privacy-enhancing tools such as mixers and privacy-focused protocols continue to evolve. Data is inherently fragmented, with on-chain activity lacking the contextual information required for identification without enrichment.

Users increasingly operate across multiple blockchains, creating fragmented activity trails that are difficult to unify. In some regions, adoption itself adds complexity. Research from the International Monetary Fund indicates that crypto participation rates in certain jurisdictions exceed 10–20% of the population, introducing scale challenges for monitoring and enforcement.

At the same time, regulatory frameworks differ across jurisdictions. Assessments by the Financial Action Task Force highlight uneven implementation of global standards such as the Travel Rule, limiting consistent cross-border data sharing and attribution.

Finally, the sheer scale of blockchain data requires robust infrastructure capable of ingesting, processing, and analyzing massive volumes of information efficiently.

Addressing these challenges requires more than isolated analytics tools. It requires a unified intelligence layer that integrates data, analytics, and compliance workflows.

Archon Insights, developed by mLogica, meets this need through its foundation on a proven, high-performance data fabric already deployed and battle-tested by tax agencies. It unifies and transforms real-time on-chain and off-chain data into a single, contextualized analytical environment, enabling regulators to move from fragmented data to actionable intelligence. By enriching blockchain data with external datasets, Archon supports entity tagging, behavioral analytics, and transaction monitoring across complex ecosystems.

The platform enables deep forensic capabilities, including multi-hop transaction tracing, while delivering real-time risk scoring and threat intelligence. It also provides built-in audit trails and automated regulatory reporting, ensuring that insights are not only actionable but also compliant and defensible.

In doing so, Archon directly addresses the core barriers faced by regulators. It simplifies data complexity through unified aggregation, accelerates time-to-insight with scalable analytics, and strengthens trust through transparent, audit-ready outputs. By transforming fragmented blockchain and external data into clear intelligence, it enables regulators to establish reliable links between wallet activity and real-world taxpayers, effectively closing the gap between blockchain transparency and regulatory accountability.

Ready to turn blockchain transparency into actionable taxpayer intelligence? Contact us to see how Archon Insights can help your agency identify entities, strengthen compliance, and close the crypto tax gap.

• Organization for Economic Co-operation and Development - Delivering Tax Transparency to Crypto-Assets: Crypto-Asset Reporting Framework (CARF)
  https://www.oecd.org/tax/transparency/documents/crypto-asset-reporting-framework.pdf
• ECB Financial Stability Review (May 2022) - ecb.europa.eu/press/financial-stability-publications/fsr/special/html/ecb.fsrart202205_02
• Bank for International Settlements - Crypto Trading and Bitcoin Prices: Evidence from a New Database of Retail Adoption
  https://www.bis.org/publ/work1049.htm
• World Bank Migration and Development Brief – Remittances
  https://www.worldbank.org/en/topic/migration/brief/remittances-knomad
• International Monetary Fund - Global Financial Stability Report – Crypto Asset Adoption and Risks
  https://www.imf.org/en/Publications/GFSR
• Financial Action Task Force - Guidance for a Risk-Based Approach to Virtual Assets and VASPs
  https://www.fatf-gafi.org/en/publications/Fatfrecommendations/Guidance-rba-virtual-assets-2021.html