Document Type
Original Article
Subject Areas
Computer Science
Keywords
Anti-Money Laundering, Clustering, Dimension reduction, Cluster verification
Abstract
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.
How to Cite This Article
Bakry, Ahmed N.; Alsharkawy, Almohammady S.; Farag, Mohamed S.; and Raslan, Kamal R.
(2024)
"Combating Financial Crimes with Unsupervised Learning Techniques: Clustering and Dimensionality Reduction for Anti-Money Laundering,"
Al-Azhar Bulletin of Science: Vol. 35:
Iss.
1, Article 1.
DOI: https://doi.org/10.58675/2636-3305.1664