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dc.contributor.advisorGarcía Bedoya, Olmer
dc.coverage.spatialColombiaspa
dc.creatorGuevara Pérez, Jorge Ivan
dc.creatorGranados, Oscar
dc.date.accessioned2021-02-12T16:57:49Z
dc.date.available2021-02-12T16:57:49Z
dc.date.created2021-02-07
dc.identifier.urihttp://hdl.handle.net/20.500.12010/17251
dc.description.abstractLas actividades de lavado de activos son el resultado de la corrupción, actividades ilegales y crimen organizado que afectan la dinámica social e involucra, directa e indirectamente a varias comunidades a través de diferentes mecanismos de blanqueo de dinero ilícito. En este artículo, proponemos un enfoque de aprendizaje automático para el análisis de actividades sospechosas en corresponsales bancarios, un tipo de agente financiero que desarrolla transacciones financieras para clientes bancarios específicos. Este artículo utiliza varios algoritmos para identificar anomalías en un conjunto de transacciones de un corresponsal bancario durante 2019 para una ciudad intermediaria en Colombia. Nuestros resultados muestran que algunas metodologías son más apropiadas que otros para este caso y facilita la identificación de las anomalías y transacciones sospechosas en este tipo de intermediario financiero.spa
dc.format.extent17 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.publisherUniversidad de Bogotá Jorge Tadeo Lozanospa
dc.sourceinstname:Universidad de Bogotá Jorge Tadeo Lozanospa
dc.sourcereponame:Expeditio Repositorio Institucional UJTLspa
dc.subjectTransaccionesspa
dc.subjectCorrupciónspa
dc.titleMachine learning methodologies against money laundering in non-banking correspondentsspa
dc.type.localTrabajo de grado de maestríaspa
dc.subject.lembLavado de activosspa
dc.subject.lembNarcotraficantesspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.type.hasversioninfo:eu-repo/semantics/acceptedVersionspa
dc.rights.localAbierto (Texto Completo)spa
dc.subject.keywordCorruptionspa
dc.identifier.repourlhttp://expeditio.utadeo.edu.cospa
dc.creator.degreeMagíster en Ingeniería y Analítica de Datosspa
dc.publisher.programMaestría en Ingeniería y Analítica de Datosspa
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dc.description.hashtag#lavadoDeActivosspa
dc.description.hashtag#TransaccionesFinancierasspa
dc.description.abstractenglishThe activities of money laundering are a result of corruption, illegal activities, and organized crime that affect social dynamics and involved, directly and indirectly, several communities through different mechanisms to launder illegal money. In this article, we propose a machine learning approach to the analysis of suspicious activities in nonbanking correspondents, a type of financial agent that develops some financial transactions for specific banking customers. This article uses several algorithms to identify anomalies in a transaction set of a nonbanking correspondent during 2019 for an intermediary city in Colombia. Our results show that some methodologies are more appropriate than others for this case and facilitate to identify the anomalies and suspicious transactions in this kind of financial intermediary.spa
dc.type.driverinfo:eu-repo/semantics/masterThesisspa
dc.type.coarhttp://purl.org/coar/resource_type/c_bdccspa


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