Combating Financial Crimes With Unsupervised Learning Techniques: Clustering and Dimensionality Reduction for Anti-Money Laundering
By Ahmed Nagy Bakry, Almohammady Sobhy Alsharkawy, Mohamed Sayed Farag, Kamal Raslan Mohamed Raslan
Rating
1385
Battle Count: 13
Relevance
6/10
While focused on anti-money laundering, the dimensionality reduction and clustering techniques could be adapted for market segmentation or anomaly detection in trading
Implementation Complexity
7/10
Requires implementation of multiple dimensionality reduction techniques and clustering algorithms, as well as careful tuning and evaluation
Reproducibility
3/5
Experimental setup and methodology are described, but specific dataset details are not provided due to confidentiality
About this paper
Methodology: Dimensionality Reduction and Clustering. Problem types: Clustering, Dimensionality Reduction, Anomaly Detection.
The interactive Everscope explorer (charts, battles, favorites) loads below.