The Data Protection Implications of the EU AML Framework: A Critical Overview & the Case of AI

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Abstract

The tension and the need for alignment between the data protection and AML/CFT framework has been pointed out by scholars and authorities since the first AMLD, over thirty years ago. Despite the criticism, none of the competent authorities has issued a pragmatic guidance aiming at consolidating the two regimes. This lack of regulatory clarity together with the fragmented implementation and interpretation of the AMLD across the EU are aggravating the challenges of the obliged entities. At the same time, the adoption of emerging technologies, such as AI for AML/CFT purposes, is further highlighting the need for harmonising the various conflicting obligations to avoid duplication and gold-plating practices. Following a doctrinal approach of primary sources, the present aims to contribute to the discussion towards a reconciliation between the data protection and AML/CFT framework from the perspective of the obliged entities, considering the recent regulatory developments, namely the AML Package and the AIA Proposal, the relevant case law of the EUCJ, and the current literature.

Original languageEnglish
Title of host publicationPrivacy Technologies and Policy - 10th Annual Privacy Forum, APF 2022, Proceedings
EditorsAgnieszka Gryszczyńska, Przemysław Polański, Nils Gruschka, Kai Rannenberg, Monika Adamczyk
PublisherSpringer Science and Business Media Deutschland GmbH
Pages37-49
Number of pages13
ISBN (Print)9783031073144
DOIs
Publication statusPublished - 2022
Event10th Annual Privacy Forum, APF 2022 - Warsaw, Poland
Duration: 23 Jun 202224 Jun 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13279 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th Annual Privacy Forum, APF 2022
Country/TerritoryPoland
CityWarsaw
Period23/06/2224/06/22

Keywords

  • AI
  • AML
  • Data protection

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