GARG-AML detects smurfing in anti-money laundering using second-order graph neighborhood densities combined with machine learning classifiers for scalable and interpretable results.
Anomaly Detection in Graphs of Bank Transactions for Anti Money Laun- dering Applications
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
ReDiRect uses fuzzy graph partitioning for distributed unsupervised money laundering detection and a refined evaluation metric, claiming better efficiency than prior methods on Libra and IBM datasets.
citing papers explorer
-
GARG-AML against Smurfing: A Scalable and Interpretable Graph-Based Framework for Anti-Money Laundering
GARG-AML detects smurfing in anti-money laundering using second-order graph neighborhood densities combined with machine learning classifiers for scalable and interpretable results.
-
Detecting Complex Money Laundering Patterns with Incremental and Distributed Graph Modeling
ReDiRect uses fuzzy graph partitioning for distributed unsupervised money laundering detection and a refined evaluation metric, claiming better efficiency than prior methods on Libra and IBM datasets.