Application of Cluster-Based Local Outlier Factor Algorithm in Anti-Money Laundering

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 facto...

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Bibliographic Details
Published in2009 International Conference on Management and Service Science pp. 1 - 4
Main Author Gao Zengan
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2009
Subjects
Algorithm design and analysis
Artificial intelligence
Clustering algorithms
Financial management
Pattern analysis
Pattern recognition
Support vector machines
Terrorism
Testing
Training data
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Summary: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.
ISBN:1424446384
9781424446384
DOI:10.1109/ICMSS.2009.5302396