Coins for Bombs: The Predictive Ability of On‐Chain Transfers for Terrorist Attacks

Dan Amiram, Bjorn Jorgensen, Daniel Rabetti*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

5 Citations (Scopus)


This study examines whether we can learn from the behavior of blockchain-based transfers to predict the financing of terrorist attacks. We exploit blockchain transaction transparency to map millions of transfers for hundreds of large on-chain service providers. The mapped dataset permits us to empirically conduct several analyses. First, we analyze abnormal transfer volume in the vicinity of large-scale highly visible terrorist attacks. We document evidence consistent with heightened activity in coin wallets belonging to unregulated exchanges and mixer services – central to laundering funds between terrorist groups and operatives on the ground. Next, we use forensic accounting techniques to follow the trails of funds associated with the Sri Lanka Easter bombing. Insights from this event corroborate our findings and aid in our construction of a blockchain-based predictive model. Finally, using machine-learning algorithms, we demonstrate that fund trails have predictive power in out-of-the sample analysis. Our study is informative to researchers, regulators, and market players, in providing methods for detecting the flow of terrorist funds on blockchain-based systems using accounting knowledge and techniques.
Original languageEnglish
Peer-reviewed scientific journalJournal of Accounting Research
Issue number2
Pages (from-to)427-466
Number of pages40
Publication statusPublished - 23.03.2022
MoE publication typeA1 Journal article - refereed


  • 512 Business and Management
  • transparency
  • terrorist financing
  • economics of blockchain
  • forensic accounting
  • bitcoin

Areas of Strength and Areas of High Potential (AoS and AoHP)

  • AoS: Financial management, accounting, and governance


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