Title :
Application of Cluster-Based Local Outlier Factor Algorithm in Anti-Money Laundering
Author_Institution :
China Center for Anti-Money Laundering Studies, Fudan Univ., Shanghai, China
Abstract :
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.
Keywords :
financial management; pattern clustering; unsupervised learning; SMLTBP recognition capability; antimoney laundering; cluster based local outlier factor algorithm; distance based unsupervised clustering; financial institution; money laundering transactional behavioral pattern; Algorithm design and analysis; Artificial intelligence; Clustering algorithms; Financial management; Pattern analysis; Pattern recognition; Support vector machines; Terrorism; Testing; Training data;
Conference_Titel :
Management and Service Science, 2009. MASS '09. International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-4638-4
Electronic_ISBN :
978-1-4244-4639-1
DOI :
10.1109/ICMSS.2009.5302396
