Combating Financial Crimes with Unsupervised Learning Techniques:
  Clustering and Dimensionality Reduction for Anti-Money Laundering
Ahmed N. Bakry Almohammady S. Alsharkawy Mohamed S. Farag Kamal R. Raslan
Anti-Money Laundering (AML) is a crucial task in ensuring the integrity of
financial systems. One keychallenge in AML is identifying high-risk groups
based on their behavior. Unsupervised learning, particularly clustering, is a
promising solution for this task. However, the use of hundreds of features
todescribe behavior results in a highdimensional dataset that negatively
impacts clustering performance.In this paper, we investigate the effectiveness
of combining clustering method agglomerative hierarchicalclustering with four
dimensionality reduction techniques -Independent Component Analysis (ICA),
andKernel Principal Component Analysis (KPCA), Singular Value Decomposition
(SVD), Locality Preserving Projections (LPP)- to overcome the issue of
high-dimensionality in AML data and improve clusteringresults. This study aims
to provide insights into the most effective way of reducing the dimensionality
ofAML data and enhance the accuracy of clustering-based AML systems. The
experimental results demonstrate that KPCA outperforms other dimension
reduction techniques when combined with agglomerativehierarchical clustering.
This superiority is observed in the majority of situations, as confirmed by
threedistinct validation indices.