Hybrid deep learning for anti-money laundering: Unsupervised detection of emerging schemes via feature fusion and explainable artificial intelligence

Traditional rule-based anti-money laundering (AML) transaction monitoring systems suffer from high false-positive rates and rigidity in detecting complex emerging risk. This limitation has prompted changes to the Financial Action Task Force (FATF) recommendation 16, mandating the use of advanced sys...

Täydet tiedot

Bibliografiset tiedot

Päätekijät:	Cosmas Ochieng Kungu, Kennedy Senagi, Evans Omondi
Aineistotyyppi:	Artigo
Kieli:	Inglês
Julkaistu:	Elsevier 2026-03-01
Sarja:	Machine Learning with Applications
Aiheet:	Anomaly detection Deep learning Feature fusion Financial crime Prioritisation
Linkit:	http://www.sciencedirect.com/science/article/pii/S2666827026000216
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