One of the greatest challenges faced by tax authorities taxing activities involving cryptoassets is identifying the parties involved.

The growth of the cryptoasset market has created a recurring challenge for TAs in identifying the various participants for tax purposes.

The problem arises because public blockchain ledgers display alphanumeric addresses and amounts, but not civil identities.

This form of pseudonymity, which differs from absolute anonymity—since all activity is permanently and immutably recorded—requires specific methods for linking addresses to individuals or legal entities when such activity constitutes a taxable event.

Public blockchains such as Bitcoin and Ethereum record every transaction in a distributed ledger that can be verified by any participant.

Unlike traditional banking systems, there is no centralized intermediary that verifies the identity of the owner of an address.

However, the ability to fully trace transaction histories makes these networks, paradoxically, a more auditable environment than cash—provided the appropriate analytical tools are available.

This feature, pseudonymity combined with full traceability, is the methodological foundation of blockchain forensic analysis techniques.

It is not necessary to break any encryption to identify behavioral patterns; rather, it is sufficient to observe the topology of the transaction network and correlate it with entry and exit points into and out of the traditional financial system, which do require identification.

The most widely used technique in blockchain forensic analysis is the “shared inputs heuristic”, which is based on the premise that if multiple addresses are used together as inputs for the same transaction, it is reasonable to assume that they belong to the same private-key holder.

Based on this and other complementary heuristics (such as reuse of change addresses and temporal activity patterns), clusters can be constructed that represent, with varying degrees of confidence, a single economic entity.

Behavioral metrics such as aggregate volume, transaction frequency, counterparty dispersion, fund turnover rate, and the proportion of funds converted to fiat currency are applied to the identified clusters.
A pattern showing high transaction frequency, multiple unrelated counterparties, and systematic conversion into fiat currency is indicative of routine trading activity, distinct from the profile of a long-term holder.

Similarly, the successive splitting of funds into transaction-obfuscation chains

(“spoof” chains) is often associated with operators that serve multiple clients, such as informal currency exchange businesses.
There are specialized platforms—both institutional and publicly accessible—that automate these heurist

ics and enrich them with databases of tagged addresses (exchanges, mixing services, sanctioned entities).

The market offers established blockchain analytics platforms: Chainalysis (used by the IRS, the DEA, and numerous law enforcement agencies in Europe and North America), TRM Labs (covering more than 100 blockchains and focusing on stablecoins on the Tron network, the most widely used for USDT transactions in Latin America), and Elliptic (a British company with a strong presence in the European financial and regulatory sectors). The World Bank and the IMF offer technical assistance programs that can subsidize access to these tools.[1]

Among institutional-use tools, commercial blockchain intelligence providers stand out, and among open-access tools, block explorers and graph visualization tools allow users to manually reconstruct the relationships between addresses.

For audit purposes, the results generated by these tools constitute circumstantial evidence that must be supplemented with institutional information to meet an adequate standard of proof.
Beyond these on-chain forensic auditing techniques, another way to identify individuals is through the various regulatory frameworks and reporting obligations that countries have already enacted.

Notable examples include the OECD’s Crypto-Asset Reporting Framework (CARF), the European Union’s DAC 8 Directive, and FATF Recommendation 16 (the Travel Rule)[2].

All of these regulations promote greater tax transparency and help detect and combat crimes such as money laundering and terrorist financing.

TAs will have greater tax oversight, as transactions involving cryptoassets must be reported, and this also entails greater regulatory compliance obligations for the parties involved.

Identifying users becomes considerably more challenging DeFi protocols, which allow users to trade, lend, or provide liquidity through smart contracts without an identifiable intermediary—a fact that undermines the regulatory foundation upon which both the Travel Rule and CARF are based, as these regulations were designed around the concept of a centralized service provider.

Other factors that make it difficult to identify individuals include fund-mixing services (mixers or tumblers), privacy-oriented cryptocurrencies, and bridges that allow assets to be transferred between different networks, thereby making it considerably more difficult to track flows by breaking the continuity of the transaction graph or by introducing cryptographic obfuscation.

The use of such tools and services, while not illegal in and of itself, constitutes a significant risk indicator for public administration bodies and financial analysis units.

In order to develop criteria for identifying the various actors, it would be useful to classify them according to the economic role they play in the cryptoasset value chain:

• – Occasional investor: acquires and holds cryptoassets as a store of value, with infrequent trading.
• – Regular trader: buys and sells on a recurring basis, seeking to take advantage of price differences. Their pattern resembles that of a de facto financial intermediary.
• – Virtual Asset Service Provider (VASP/CASP): centralized exchanges, P2P exchange platforms, and custodians that facilitate the conversion between cryptoassets and fiat currency.
• – Miner or validator: earns cryptoassets as a compensation for validating transactions and securing the network(proof-of-work or proof-of-stake), which constitutes income with its own triggering event.Liquidity provider and participant in DeFi protocols: deposits assets into smart contracts in exchange for fees or governance tokens, with no identifiable intermediary.
• – Non-fungible token (NFT) trader and creator of tokenized content: generates revenue from the first sale and royalties programmed into the contract itself.

I understand that this proposed taxonomy is not exhaustive, since a single individual may combine several roles, and the tax classification (ordinary income, capital gain, business activity) depends on the laws of each jurisdiction and on the regularity, professionalism, and for-profit nature of the activity.

The transnational nature of public blockchains requires that the identification of parties be based on cooperation mechanisms among tax administrations, given that a service provider may be domiciled in a jurisdiction other than that of the user.
For this reason, we agree with Raul Zambrano in this same blog post, which emphasizes the need for greater cooperation among tax administrations.[3]

Based on the foregoing, it is proposed that the TAs assess the existence of regular cryptoasset trading activity by combining indicators such as:

• – Frequency and volume: number of transactions per period and aggregate amount relative to the taxpayer’s reported net worth.
• – Counterparty diversity: the number of unrelated addresses with which the identified cluster interacts.
• – Conversion to fiat currency: the proportion of funds converted through regulated service providers
• – Institutional Reporting: Correspondence between the information reported under CARF and Travel Rule requirements by VASPs and the activity detected in the on-chain analysis.
• – Publicity of the activity: the existence of advertisements or public profiles offering services for the purchase and sale of cryptoassets.

No single indicator is conclusive; rather, its probative value arises from the convergence of on-chain evidence and institutional information, which reinforces the need for tax authorities to develop their own technical capabilities for blockchain analysis, in parallel with the implementation of information-sharing frameworks.

Ultimately, identifying actors who trade cryptoassets on public blockchains for tax purposes requires a combined approach.

On-chain analysis makes it possible to detect behavioral patterns consistent with normal economic activity, while off-chain regulatory frameworks—in particular CARF, DAC8, and the FATF Travel Rule—provide the civil identification link that blockchain, by design, does not provide.

The effectiveness of this framework will depend on the speed and consistency with which the various jurisdictions adopt these standards, as well as on the ability of tax authorities to adapt their methodologies to increasingly decentralized environments, where the role of the regulated intermediary—on which traditional tax compliance relies—tends to diminish.

Reference:

[1] Porporatto, Pablo; “Tax Oversight of Cryptoassets and Digital Platforms: Tax Risks and Technological Opportunities”https://www.ciat.org/control-fiscal-de-criptoactivos-y-plataformas-digitales-riesgos-tributarios-y-oportunidades-tecnologicas/

[2] Collosa, Alfredo;  “CARF, MiCA, DAC 8, and the Travel Rule are paving the way for greater transparency in the cryptoasset market” https://www.ciat.org/carf-mica-dac-8-y-la-travel-rule-avanzan-hacia-mayor-transparencia-en-el-mercado-de-criptoactivos/

[3] Zambrano Raul, A Vampire Story (VI) https://www.ciat.org/ciatblog-una-historia-de-vampiros-vi/

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