Wolfsberg Group Creates Deeply Flawed "Definitional Hierarchy" For ML/TF Risks In Digital Assets

The Wolfsberg Group's FAQs on digital assets aim to align global efforts in countering money laundering and terrorist financing risks.

Wolfsberg Group Creates Deeply Flawed "Definitional Hierarchy" For ML/TF Risks In Digital Assets
Photo by Marija Zaric / Unsplash
  • The Wolfsberg Group released an FAQ on the financial crime risks of digital assets aiming to create a "definitional hierarchy" to align assessments in AML/CFT between banks and policy makers.
  • The Group has not responded to requests for comments on its deeply flawed definition of terms.

The Wolfsberg Group, an association of 12 global banks that aims to develop frameworks and guidance for the management of financial crime risks, has released Frequently Asked Questions (FAQs) on Digital Assets.

The document aims to create a "definitional hierarchy" of terms it deems "critical to assessing the Money Laundering (ML) and Terror Financing (TF) risks of digital assets."

The FAQs identify privacy-enhancing features of digital assets as a significant source of increased money laundering and terrorist financing risks, alleging that these risks stem from the inherent anonymity in certain cryptocurrencies, privacy-focused enhancements, and technologies such as mixers, tumblers, and zero-knowledge proofs that obfuscate transaction origins and destinations.

The resulting opacity could undermine traditional monitoring frameworks and potentially make it challenging to trace illicit financial flows, the Group states.

Defining "Anonymity-Enhanced Cryptocurrencies" and "Privacy Enhancers"

The FAQs highlight what it calls Anonymity-Enhanced Cryptocurrencies (AECs) like Monero and Zcash, which favor "the anonymity of users, the opacity of financial flows [...], and can be traded anonymously," arguing that AECs present "heightened ML/TF risks due to the inherent lack of payment transparency, even where the use of AEC privacy features is marketed as optional."

The Group identifies AECs as employing "a variety of strategies", such as "use of one time wallet addresses," "use of mixer technology," and "chain hopping across multiple currencies sometimes referred to as atomic swaps."

The Wolfsberg Group defines the term "Privacy Enhancers", which it states to be "an AEC protocol or software extensions designed to protect the anonymity of users and the opacity of financial flows, or to enable anonymous trading," as ""blockchain-based digital assets" that "authenticate and relay transactions across network nodes which validate transactions", while also noting that "some jurisdictions may classify certain privacy enhancers as a mixer or tumbler."

The Group distinguishes Privacy Enhancers from "mixers/tumblers", which it defines as "services used to obfuscate the source and destination of digital asset transactions".

The Wolfsberg Group claims that CoinJoin, Whirlpool, Ring Signatures and ZkSNARKS are examples of Privacy Enhancers by its own definition. However, the allegation that CoinJoin or Whirlpool, which are technically the same thing, "authenticate and relay transactions" appears incorrect, as the named improvements don’t actually handle or change how transactions are sent (relayed) or checked (validated) on the blockchain.

The Group's FAQ further states that there are four "primary methods" to implement privacy enhancer protocols or software: native tokens, hard forks, soft forks, and extension blocks. Again, neither CoinJoins nor Whirlpool would fall under such implementation methods.

The Rage has reached out to the Wolfsberg Group for a clarification on the definition of privacy enhancers, asking the Group to elaborate on the reasoning behind such categorizations and to clarify how these protocols fit within—or fall outside—their presented definitions of privacy enhancers.

CoinJoin is an Anonymity-Enhanced Cryptocurrency, but Mixers/Tumblers are not

The Wolfsberg Group states that "a mixer, also known as a tumbler, is not an AEC," stating that a mixer obfuscates activity on the ledger for example by "creating and using single-use wallets, addresses, or accounts, and sending digital assets through such wallets, addresses, or accounts through a series of independent transactions" or "facilitating user-initiated delays in transactional activity."

The description of mixers (or tumblers) as mechanisms that obfuscate ledger activity—e.g., by creating single-use wallets, facilitating user-initiated delays, or sending assets through multiple independent transactions—seems to describe the technical capabilities of many cryptocurrency wallets, not solely mixers.

The Rage has reached out to the Wolfsberg Group to clarify how their definition distinguishes mixers from general-purpose wallets.

In examples of mixers, the Wolfsberg Group's FAQ defines Samourai Wallet and Wasabi Wallet as "Bitcoin-Laundry Services", while previously having described CoinJoin and Whirlpool, which again are the same thing and employed by both wallets named, as Anonymity-Enhanced Cryptocurrencies.

The Rage has reached out to the Wolfsberg Group for clarification on why they would define these wallets as "Bitcoin-Laundry Services," asking how the Group accounts for the widely documented legitimate use of privacy software in digital assets in their analysis.

As previously reported by The Rage, The Wolfsberg Group has criticized current Anti-Money Laundering (AML) practices as inefficient and ineffective in identifying and preventing financial crime, noting an increase in the number of Suspicious Activity Reports (SARs) filed, while citing little evidence that these reports yield actionable intelligence or significantly deter money laundering or terrorism financing.

In response, the Wolfsberg Group proposed a shift to a "True Risk-Based Approach" that would involve leveraging dynamic customer data, such as device IDs and IP addresses as well as behavioral customer data such as publicly available social media information, to improve customer behavior analysis and create a more contextual customer profile, suggesting Machine Learning as a tool to enhance or even replace current rules-based systems with an emphasis on future opportunities for public and private partnerships.

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