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Assessing the Impact of Sanctions in the Crypto Ecosystem: Effective Measures or Ineffective Deterrents?

Francesco Zola, Jon Ander Medina, Raul Orduna

TL;DR

This paper investigates whether financial sanctions effectively deter illicit crypto activity, focusing on Bitcoin addresses associated with OFAC-listed entities. It builds a graph-based framework that tracks address–transaction flows across pre- and post-sanction windows and conducts 1-step and 2-step behavioural analyses to map connections to known crypto services and illicit actors. Key findings show that sanctions deter about half of the entities, but the rest continue moving funds through sanctioned addresses, with a tendency to use regulated exchanges for off-ramp conversions rather than mixers or gambling services. The study highlights the value and limits of current sanctions in the crypto ecosystem, underscores the need for multi-asset and heuristic labelling approaches, and suggests tighter coordination across regulators and service providers to improve traceability and deterrence in practice.

Abstract

Regulatory authorities aim to tackle illegal activities by targeting the economic incentives that drive such behaviour. This is typically achieved through the implementation of financial sanctions against the entities involved in the crimes. However, the rise of cryptocurrencies has presented new challenges, allowing entities to evade these sanctions and continue criminal operations. Consequently, enforcement measures have been expanded to include crypto assets information of sanctioned entities. Yet, due to the nature of the crypto ecosystem, blocking or freezing these digital assets is harder and, in some cases, such as with Bitcoin, unfeasible. Therefore, sanctions serve merely as deterrents. For this reason, in this study, we aim to assess the impact of these sanctions on entities' crypto activities, particularly those related to the Bitcoin ecosystem. Our objective is to shed light on the validity and effectiveness (or lack thereof) of such countermeasures. Specifically, we analyse the transactions and the amount of USD moved by punished entities that possess crypto addresses after being sanctioned by the authority agency. Results indicate that while sanctions have been effective for half of the examined entities, the others continue to move funds through sanctioned addresses. Furthermore, punished entities demonstrate a preference for utilising rapid exchange services to convert their funds, rather than employing dedicated money laundering services. To the best of our knowledge, this study offers valuable insights into how entities use crypto assets to circumvent sanctions.

Assessing the Impact of Sanctions in the Crypto Ecosystem: Effective Measures or Ineffective Deterrents?

TL;DR

This paper investigates whether financial sanctions effectively deter illicit crypto activity, focusing on Bitcoin addresses associated with OFAC-listed entities. It builds a graph-based framework that tracks address–transaction flows across pre- and post-sanction windows and conducts 1-step and 2-step behavioural analyses to map connections to known crypto services and illicit actors. Key findings show that sanctions deter about half of the entities, but the rest continue moving funds through sanctioned addresses, with a tendency to use regulated exchanges for off-ramp conversions rather than mixers or gambling services. The study highlights the value and limits of current sanctions in the crypto ecosystem, underscores the need for multi-asset and heuristic labelling approaches, and suggests tighter coordination across regulators and service providers to improve traceability and deterrence in practice.

Abstract

Regulatory authorities aim to tackle illegal activities by targeting the economic incentives that drive such behaviour. This is typically achieved through the implementation of financial sanctions against the entities involved in the crimes. However, the rise of cryptocurrencies has presented new challenges, allowing entities to evade these sanctions and continue criminal operations. Consequently, enforcement measures have been expanded to include crypto assets information of sanctioned entities. Yet, due to the nature of the crypto ecosystem, blocking or freezing these digital assets is harder and, in some cases, such as with Bitcoin, unfeasible. Therefore, sanctions serve merely as deterrents. For this reason, in this study, we aim to assess the impact of these sanctions on entities' crypto activities, particularly those related to the Bitcoin ecosystem. Our objective is to shed light on the validity and effectiveness (or lack thereof) of such countermeasures. Specifically, we analyse the transactions and the amount of USD moved by punished entities that possess crypto addresses after being sanctioned by the authority agency. Results indicate that while sanctions have been effective for half of the examined entities, the others continue to move funds through sanctioned addresses. Furthermore, punished entities demonstrate a preference for utilising rapid exchange services to convert their funds, rather than employing dedicated money laundering services. To the best of our knowledge, this study offers valuable insights into how entities use crypto assets to circumvent sanctions.
Paper Structure (15 sections, 5 figures, 3 tables)

This paper contains 15 sections, 5 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Background
  3. EU Directive review
  4. US Office of Foreign Assets Control
  5. Cryptocurrency and Cybercrime
  6. Experimental Framework
  7. Sanctions List
  8. Bitcoin Dataset
  9. Proposed Analysis
  10. Current Study
  11. Flow Analysis Results
  12. Behavioural Analysis Results
  13. Discussion and Limitations
  14. Conclusion
  15. Acknowledgments.

Figures (5)

  • Figure 1: Overall statistics of sanctioned entities with cryptocurrency information extracted from the SDN list (February 2024).
  • Figure 2: Overview about violations of sanctioned entities with BTC information extracted from the SDN list (February 2024).
  • Figure 3: An example of a 1-step address-transaction graph
  • Figure 4: Number of sanctioned entities that perform transactions (received and sent) in the different post-sanction intervals.
  • Figure 5: Entity balance (in BTC) considering the sanctioned addresses at the four stages: pre-sanction, 7 post-sanction, 30 post-sanction, and up-to-date.