The effect of anti-money laundering policies: an empirical network analysis

Abstract

Aim 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. Method 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. Findings 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.

A large-scale empirical investigation of specialization in criminal career

“…We can further locate specialists and generalists within their criminal collaboration networks and interpret these positions. Our work is part of an emerging field that applies the methods of data and network science 56 – 58 to study criminal behavior 29 , 59 – 61 .…”

“…We can further locate specialists and generalists within their criminal collaboration networks and interpret these positions. Our work is part of an emerging field that applies the methods of data and network science [56][57][58] to study criminal behavior 29,[59][60][61] . www.nature.com/scientificreports/ Our method to cluster crime types extends a statistical method from Tumminello et al 32 .…”

Global Developments in Trade-Based Money Laundering

“…Adaptation by money launderers Gerbrands, et al (2022) analyse a large administrative dataset provided by the Government of the Netherlands, finding evidence that enhanced money-laundering legislation in the European Union since 2015 has affected how the money-laundering industry operatesleading to increased specialisation, expansion of money-laundering networks and greater internationalisation of companies associated with money laundering. In effect, this indicates that enhanced money-laundering regulations have led to a countervailing increase in the complexity and professionalism of money-laundering activity.…”

Ciberdelito y su asociación en el cometimiento de fraudes financieros en la pandemia de la COVID-19

“…Las organizaciones criminales van innovándose en metodologías para efectuar lavado de dinero, fraude y delitos financieros, usando nuevas tecnologías, especialmente plataformas en línea y sistemas de información empresariales (Gerbrands et al, 2022). Gestándose ciberdelitos, que en la pandemia se manifestaron en las actividades rutinarias realizadas por las organizaciones en sistemas en línea, como el robo de identidad, falsificación de información bancaria, compras en línea fraudulentas y virus informáticos, que figuraron como comportamientos ilícitos que generaron millonarias pérdidas financieras y utilizando de manera efectiva delitos cibernéticos (Hawdon et al, 2020).…”