The effect of anti-money laundering policies: an empirical network analysis
With the support of iCOV and in collaboration with Brigitte Unger, Michael Getzner and Joris Ferwerda, I am happy to announce that on March 18, 2022, we have published a new article in EPJ Data Science on money laundering networks. This paper uses a unique temporal data set with information on corporations, people and criminal records which allows us to compare how expected criminal networks develop in contrast to non-criminal networks. In hsort, we demonstrate that anti-money laundering policies work by increasing the complexity to launder money and criminals respond by incorporating more companies.
There is a growing literature analyzing money laundering and the policies to fight it, but the overall effectiveness of anti-money laundering policies is still unclear. This paper investigates whether anti-money laundering policies affect the behavior of money launderers and their networks.
With an algorithm to match clusters over time, we build a unique dataset of multi-mode, undirected, binary, dynamic networks of natural and legal persons. The data includes ownership and employment relations and associated financial ties and is enriched with criminal records and police-related activities. The networks of money launderers, other criminals, and non-criminal individuals are analyzed and compared with temporal social network analysis techniques and panel data regressions on centrality measures, transitivity and assortativity indicators, and levels of constraint.
We find that after the announcement of the fourth EU anti-money laundering directive in 2015, money laundering networks show a significant increase in the use of foreigners and corporate structures. At the individual level, money launderers become more dominant in criminal clusters (increased closeness centrality). This paper shows that (the announcement of) anti-money laundering policies can affect criminal networks and how such effects can be tested.