Vector support machines and classification trees for detecting suspicious operations of money laundering
DOI:
https://doi.org/10.21501/21454086.2904Keywords:
Classification trees, Vector Support, Metrics, Precision, Financial entities, False positives, Money laundering, Unusual operations, Suspicious operations, Detection.Abstract
Money laundering is a crime that brings a large number of negative consequences to society in general. Anti-money laundering systems have been developed to mitigate this problem in financial institutions, which is where it is mainly presented. This causes a new problem: the false positives obtained from these systems, which represent financial losses for the financial entities, as well as time and focus, since they do not deal with the real unusual operations. The main detection methods of unusual operations of money laundering found in the literature are evaluated to determine which techniques offer the best results and from these generate a new model that improves the registered indicators. From a process of review and replication of anomalies detection methodologies found in the literature, a new model that presents better metrics when classifying operations as normal and unusual could be generated, this may represent way to reduce the false positive rates in their anti-money laundering systems in financial institutions.Downloads
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