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

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Part of the Lecture Notes in Computer Science book series (LNSC,volume 13279)

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.

Keywords

  • AML
  • Data protection
  • AI

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Notes

  1. 1.

    Without prejudice to the data identified in relation to beneficiary owners in Article 30 AMLD (i.e. date of birth and contact details) and the information contained in central registries by the Member States, pursuant to Article 32a AMLD.

  2. 2.

    Interestingly so, aside from a general reference to complying with the data protection requirements, such as data localisation and security, FATF in its AML/CFT Guidance and Methodologies, when it comes to data protection it only expressly refers to the confidentiality and data protection obligations of competent authorities [18, p. 111; 19, p. 93].

  3. 3.

    Similar observation has been made by FATF in its Methodology for Assessing Technical Compliance with FATF Recommendations and the Effectiveness of AML/CFT Systems [18, p. 22].

  4. 4.

    See Sect. 3 below regarding processing of certain categories of data and Sect. 4 regarding outsourcing.

  5. 5.

    C-311/18 - Facebook Ireland and Schrems, available https://curia.europa.eu/juris/liste.jsf?num=C-311/18.

  6. 6.

    The same conclusion is drawn by the EDPB regarding silent party data. In this regard, note that PSD2 states that data collected should only be used for the purpose of providing the requested services.

  7. 7.

    See Sect. 3 below.

  8. 8.

    See among others, Case C-136/17, C.G, CURIA - Documents (europa.eu).

  9. 9.

    Watchlists as refereed in the Letter of the EBPD [14, p. 5].

  10. 10.

    See Sect. 4 below.

  11. 11.

    Similarly, in the AML Proposal (Recital 62 and Article 40(1).

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Correspondence to Iakovina Kindylidi .

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Kindylidi, I. (2022). The Data Protection Implications of the EU AML Framework: A Critical Overview & the Case of AI. In: Gryszczyńska, A., Polański, P., Gruschka, N., Rannenberg, K., Adamczyk, M. (eds) Privacy Technologies and Policy. APF 2022. Lecture Notes in Computer Science(), vol 13279. Springer, Cham. https://doi.org/10.1007/978-3-031-07315-1_3

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  • DOI: https://doi.org/10.1007/978-3-031-07315-1_3

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