| Summary: | This dissertation constitutes a systems theoretical analysis of Anti-Money Laundering that dismisses the projected ideals of holism and delves into the core of Systems Theory (ST) in the tradition of second-order cybernetics. This theoretical approach of ST is appropriated in order to describe the domain of Anti-Money Laundering (AML) as a system in itself and at the same time examine the consequences that technology comes to play within the system of AML. While the contemporary phenomenon of AML has been reduced mostly into a set of technological consequences from profiling technologies (technologies that attempt within financial institutions to model and simulate money-laundering behaviour for the generation of suspicious transactions), this dissertation takes a different approach. Instead of focusing at profiling technologies that are believed to be the core technological artefacts that influence AML within financial institutions, this dissertation examines a variety of information systems and their interplay and describes through empirical findings the multitude of interactions that are technologically supported and that construct a much more complex picture of dealing with AML and thereby influencing how money-laundering is perceived. The empirical findings supporting the theoretical treatise come from a longitudinal case study of a Greek financial institution where a systematic examination takes place regarding a variety of information systems that may affect AML within the bank. Beyond isolated interferences of information systems to AML, their interrelations are further examined in order to reflect on the emergent complexity that often distorts cause-and-effect AML manipulations. The theoretical contributions put forward, constitute a systems theoretical application and an expansion of technological/systemic interferences, while the practical contributions to AML cover broader systems-theoretical reflections on the domain, technological integration within financial institutions for targeting ML, feedback relations between financial institutions and Financial Intelligence Units, as well as the systemic consequences for the newly implemented risk-based approach.
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