Terror on the Blockchain: The Emergent Crypto-Crime-Terror Nexus
Bibliographic record
Abstract
Abstract Inadequate oversight and an inchoate appreciation are giving terrorist groups ready access to transboundary financial transfers by means of virtual currency. This chapter counters the prevailing approach that treats cryptocurrency-enabled crimes, such as terrorism, as monolithic. This chapter demonstrates that terrorist groups are using cryptocurrency and decentralized finance to fundraise and transfer funds in conjunction with the traditional financial system. Since actual case studies are few and data limited, this chapter is a proof of concept: it compares terrorist financing schemes by the Al-Qassam Brigades and Al Qaeda that used virtual assets. The comparison of virtual assets being used finds that standards developed and recommended by the Financial Action Task Force (FATF) are wholly inadequate to contain the proliferation of decentralized finance technology and centralized virtual assets as drivers of the global Illicit International Political Economy (IIPE). FATF recommendations are not sufficiently nuanced, nor are they effective at detecting, disrupting and deterring he nexus of crypto, crime and terror. To make matters worse, FATF members are falling short on implementing even FATF’s inadequate standards. The chapter concludes that FATF needs to: clarify inclusion criteria under the current definition of virtual assets; broaden regulations, improve interagency collaboration, and formulate more nuanced recommendations that are sensitive to crypto-enabled crimes across different criminal activities and criminogenic factors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".