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Explainable Feature Engineering for Multi-class Money Laundering Classification

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  • Petre-Cornel GRIGORESCU
  • Antoaneta AMZA

Abstract

This paper provides insight into typical money laundering typologies used in the financial crime domain and provides a concrete set of methods through the use of which fraudulent transactions may be classified using traditional machine learning algorithms and proving the efficacy of tree-based models in not only predictive power, but also explainability and ease of interpretation of results.

Suggested Citation

  • Petre-Cornel GRIGORESCU & Antoaneta AMZA, 2025. "Explainable Feature Engineering for Multi-class Money Laundering Classification," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 29(1), pages 64-77.
    • Handle: RePEc:aes:infoec:v:29:y:2025:i:1:p:64-77
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    References listed on IDEAS

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    1. Ogbeide, Henry & Thomson, Mary Elizabeth & Gonul, Mustafa Sinan & Pollock, Andrew Castairs & Bhowmick, Sanjay & Bello, Abdullahi Usman, 2023. "The anti-money laundering risk assessment: A probabilistic approach," Journal of Business Research, Elsevier, vol. 162(C).
    2. ., 2025. "Structuring a story," Chapters, in: How to Use Storytelling in Your Academic Writing, chapter 2, pages 7-20, Edward Elgar Publishing.
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