A Holistic Network Analysis of the Money Laundering Threat Landscape: Assessing Criminal Typologies, Resilience and Implications for Disruption

Abstract

Abstract Objective This article visualises money laundering, a crime spanning hundreds of different actors, methods and value instruments, as a network. Possible combinations of money laundering actions and schemes are analysed through a holistic visualisation to draw policy-relevant insights into their prevention. Methods A prior scoping review of money laundering typologies identified 793 activities that a money launderer can take to obfuscate illicit funds. These actions form the basis of a directed network graph showcasing all possible combinations of money laundering schemes. Three network-based analyses are then conducted: (1) centrality analysis, to determine the most ‘important’ money laundering actions as priorities for prevention, (2) resilience analysis to simulate iterative interventions against different money laundering actions to assess their impact on the wider network and (3) subgroup analysis to identify groups of commonly undertaken schemes (i.e. “typologies”). Results Money laundering is found to be a highly resilient process crime, with specific interventions often unable to prevent schemes from displacing to alternative activities to launder their funds. However, benefits of tactically displacing criminals to more easily detectable schemes are discussed. The formulation of money laundering typologies through more empirically robust processes is also motivated, in place of compartmentalised reports that remain the current standard practice. Conclusions Network analysis is motivated as an effective way of visualising complex process crimes with vast criminal opportunities such as money laundering, allowing policy-relevant insights to be drawn in terms of resource-allocation, strategy and prioritisation. Theoretical and policy implications of the current study are discussed in the context of crime scientific theories.