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The power of data: Transforming compliance with anti-money laundering measures in domestic and cross-border payments

Author

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  • Rozman, Alexander G.

    (Chief Compliance Officer, Exodus Movement Inc., USA)

Abstract

Financial crime is on the rise. The interconnectedness of global payments is making illicit cross-border transactions increasingly frictionless, while digitisation has not only increased the attack surface but also enabled criminals to employ increasingly sophisticated methods to evade detection, such as social engineering. Compounding the threat is the ever-growing mountain of data being generated, which is threatening to drown detection and prevention efforts. It is clear that a new level of defence is required in the fight against financial crime. This paper argues that the implementation of data-driven technologies, with a focus on artificial intelligence (AI), is a near-term imperative for effective risk-based anti-money laundering (AML) compliance programmes in the payments industry, where legacy AML programmes over-rely on a rule-based approach with a checklist of predetermined rules and indicators to flag potentially suspicious activity. To support the development and implementation of such data-driven enhancements, this paper hones in on the current hurdles, and explains the benefits and technical aspects of AI integration, emphasising the foundational importance of ISO/IEC AI-related standards. Finally, the paper discusses future opportunities for broader collaboration to build stronger collective defences.

Suggested Citation

  • Rozman, Alexander G., 2024. "The power of data: Transforming compliance with anti-money laundering measures in domestic and cross-border payments," Journal of Payments Strategy & Systems, Henry Stewart Publications, vol. 18(3), pages 253-260, September.
  • Handle: RePEc:aza:jpss00:y:2024:v:18:i:3:p:253-260
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    Citations

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    Cited by:

    1. Qian Yu & Zhen Xu & Zong Ke, 2024. "Deep Learning for Cross-Border Transaction Anomaly Detection in Anti-Money Laundering Systems," Papers 2412.07027, arXiv.org.

    More about this item

    Keywords

    data; artificial intelligence; machine learning; anti-money laundering; compliance; ISO standards;
    All these keywords.

    JEL classification:

    • G2 - Financial Economics - - Financial Institutions and Services
    • E5 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit

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