Disrupting Criminal Networks: A Scoping Review of Network Disruption Methods
Dayle Harvey, Jasmine Madjlessi, Christophe Vandeviver · CrimRxiv · 2025
Background: Criminal networks pose a persistent challenge to law enforcement agencies due to their adaptability, resilience, and decentralized structures. Traditional disruption efforts, such as high-profile arrests and asset seizures, often yield short-term success but fail to address systemic resilience, enabling criminal organizations to reconfigure and persist. To effectively dismantle these networks, law enforcement requires evidence-based strategies that target structural vulnerabilities while minimizing unintended consequences. Methods: This scoping review examines the current landscape of criminal network disruption strategies, synthesizing evidence from 36 studies to (a) identify existing disruption approaches, (b) evaluate their reported effects on network fragmentation and weakening, and (c) explore the potential role of forensic intelligence in enhancing disruption efforts. We conducted a search across academic databases, applying strict inclusion criteria to identify empirical studies on network disruption interventions. Results: While some strategies effectively fragmented networks, others proved largely ineffective. Forensic intelligence (FI) emerged as a promising tool for testing disruption approaches. Forensic intelligence offers potential insights through data to optimize intervention points. Currently, forensic intelligence remains underutilized in network disruption studies, highlighting a significant gap in research and practice. Conclusions: Criminal network disruption requires targeted, data-driven approaches to mitigate network resilience and adaptation. Future research could explore longitudinal studies and machine learning applications to predict network responses to interventions. Expanding forensic intelligence integration within intelligence-led policing frameworks may significantly enhance law enforcement's ability to dismantle illicit networks effectively.Introduction