Harnessing AI for AML/CFT: Legal grounds for training AI on personal data for AML/CFT under EU data protection law

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Abstract

AI systems can assist in fulfilling AML/CFT obligations within the revised EU AML framework. To function accurately, these AI-enhanced AML systems require extensive training on datasets, including personal data. This paper examines the legal grounds under the General Data Protection Regulation (GDPR) for processing such data, with a focus on compliance with legal obligations [Article 6(1)(c) GDPR] and legitimate interest [Article 6(1)(f) GDPR]. The paper argues that, while legal obligation may not provide a sufficient basis due to the lack of explicit mandates requiring AI use, legitimate interest presents a viable alternative, dependent on a rigorous test. By scrutinising the necessity of balancing financial institutions’ need for AI-enhanced AML/CFT tools with EU data protection law, this paper underscores the significance of safeguards to mitigate risks associated with such tools, including bias, transparency shortcomings, and challenges in exercising data subject rights.
Original languageEnglish
Pages (from-to)72-92
Number of pages21
JournalInformation & Communications Technology Law
Volume35
Issue number1
Early online date31 May 2025
DOIs
Publication statusPublished - 2026

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 10 - Reduced Inequalities
    SDG 10 Reduced Inequalities
  2. SDG 17 - Partnerships for the Goals
    SDG 17 Partnerships for the Goals

Keywords

  • anti-money laundering
  • AML
  • CFT
  • AI
  • legal grounds
  • personal data processing

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