Developing an intelligent data discriminating system of anti-money laundering based on SVM

• Published in: 2005 International Conference on Machine Learning and Cybernetics Vol. 6; pp. 3453 - 3457 Vol. 6
• Main Authors: Jun Tang, Jian Yin
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2005
• Subjects: Anti-money laundering; Cities and towns; Computer science; Computerized monitoring; Information technology; Intelligent systems; Machine learning algorithms; Pattern recognition; Regulators; SLT; Statistical learning; support vector machine; Support vector machines
• ISBN: 0780390911; 9780780390911
• ISSN: 2160-133X
• DOI: 10.1109/ICMLC.2005.1527539

Statistical learning theory (SLT) is introduced to improve the embarrassments of anti-money laundering (AML) intelligence collection. A set of unusual behavior detection algorithm is presented in this paper based on support vector machine (SVM) in order to take the place of traditional predefined-rule suspicious transaction data filtering system. It could efficiently surmount the worst forms of suspicious data analyzing and reporting mechanism among bank branches including enormous data volume, dimensionality disorder with massive variances and feature overload.