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

Abstract

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 data set 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-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.

Keywords: transparency, terrorist financing, economics of blockchain, forensic accounting, bitcoin

JEL Classification: G15, G18, G29, K29, K42, M40, M41, O16