Today, money laundering is not only a threat to national financial institutions. These criminal activities are becoming increasingly complex, and drug trafficking certainly does not forget personal interests, and terrorist financing seems to have moved from being commonplace. Banking sectors refresh the suspicious using fraud detection techniques Like AML to screen the process. However, traditional intelligence techniques consume a large amount of man-hours. Recently, data mining algorithms have been developed, and the technique used to detect money in frauds is very relevant. For the purpose of money laundering process become very close to suspicious because of business malpractices need advancement to process (Anti-money)AML, we propose Identify the crime financial activities on money laundering using discriminant data flow analysis based on Activity support vectors (D2FA-ASV) in transactional Log. This produce an effective anti-money laundering scheme based on active data clustering clustering. In this study, the new intents enhance the possibilities of Intrusions in money laundering suspicious by Knowledge learning clustering evaluation to produce better screening classification in Transactional data logs.
Volume 12 | 03-Special Issue
Pages: 864-875
DOI: 10.5373/JARDCS/V12SP3/20201328