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<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/jajl7qtqc5cy7oavratsldrv2y" style="color: black;">Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering</a>
Today, money laundering (ML) poses a serious threat not only to financial institutions but also to the nation. This criminal activity is becoming more and more sophisticated and seems to have moved from the cliché of drug trafficking to financing terrorism and surely not forgetting personal gain. Most international financial institutions have been implementing anti-money laundering solutions (AML) to fight investment fraud. However, traditional investigative techniques consume numerous<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-642-11534-9_8">doi:10.1007/978-3-642-11534-9_8</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/r6367g6i2bhqtgs3q6lqvmcqae">fatcat:r6367g6i2bhqtgs3q6lqvmcqae</a> </span>
more »... . Recently, data mining approaches have been developed and are considered as well-suited techniques for detecting ML activities. Within the scope of a collaboration project for the purpose of developing a new solution for the AML Units in an international investment bank based in Ireland, we propose a new data mining-based approach for AML. In this paper, we present this approach and some preliminary results associated with this method when applied to transaction datasets.
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