Application of Cluster-Based Local Outlier Factor Algorithm in Anti-Money Laundering - Gao Zengan (2009 International Conference on Management and Service Science, 2009)
Financial institutions' capability in recognizing suspicious money laundering transactional behavioral patterns (SMLTBPs) is critical to antimoney laundering. Combining distance-based unsupervised clustering and local outlier detection, this paper designs a new cluster based local outlier factor (CBLOF) algorithm to identify SMLTBPs and use authentic and synthetic data experimentally to test its applicability and effectiveness.