Explainable Feature Engineering for Multi-class Money Laundering Classification

Petre-Cornel Grigorescu () and Antoaneta Amza ()

Informatica Economica, 2025, vol. 29, issue 1, 64-77

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.

Keywords: Anti-money laundering; Machine learning; Tree-based models; Explainability (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:aes:infoec:v:29:y:2025:i:1:p:64-77

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