Money laundering and terrorism financing detection using neural networks and an abnormality indicator

This study proposes a comprehensive model that helps improve self-comparisons and group-comparisons for customers to detect suspicious transactions related to money laundering (ML) and terrorism financing (FT) in financial systems. The self-comparison is improved by establishing a more comprehensive...

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Detalles Bibliográficos
Autores: Rocha Salazar, José de Jesús, Segovia Vargas, María Jesús, Camacho Miñano, Juana María Del Mar
Tipo de recurso: artículo
Fecha de publicación:2021
País:España
Institución:Universidad Complutense de Madrid (UCM)
Repositorio:Docta Complutense
Idioma:inglés
OAI Identifier:oai:docta.ucm.es:20.500.14352/8075
Acceso en línea:https://hdl.handle.net/20.500.14352/8075
Access Level:acceso abierto
Palabra clave:Money laundering
Financing of terrorism
Unsupervised learning
Detection
Machine Learning.
Dinero
5304.06 Dinero y Operaciones Bancarias
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oai_identifier_str oai:docta.ucm.es:20.500.14352/8075
network_acronym_str ES
network_name_str España
repository_id_str
spelling Money laundering and terrorism financing detection using neural networks and an abnormality indicatorRocha Salazar, José de JesúsSegovia Vargas, María JesúsCamacho Miñano, Juana María Del MarMoney launderingFinancing of terrorismUnsupervised learningDetectionMachine Learning.Dinero5304.06 Dinero y Operaciones BancariasThis study proposes a comprehensive model that helps improve self-comparisons and group-comparisons for customers to detect suspicious transactions related to money laundering (ML) and terrorism financing (FT) in financial systems. The self-comparison is improved by establishing a more comprehensive know your customer (KYC) policy, adding non-transactional characteristics to obtain a set of variables that can be classified into four categories: inherent, product, transactional, and geographic. The group-comparison involving the clustering process is improved by using an innovative transaction abnormality indicator, based on the variance of the variables. To illustrate the way this methodology works, random samples were extracted from the data warehouse of an important financial institution in Mexico. To train the algorithms, 26,751 and 3527 transactions and their features, involving natural and legal persons, respectively, were selected randomly from January 2020. To measure the prediction accuracy, test sets of 1000 and 600 transactions were selected randomly for natural and legal persons, respectively, from February 2020. The proposed model manages to decrease the proportion of false positives and increase accuracy when compared to the rule-based system. On reducing the false positive rate, the company’s costs for investigating suspicious customers also decrease significantly.ElsevierUniversidad Complutense de Madrid20212021-01-0120212021-01-01journal articlehttp://purl.org/coar/resource_type/c_6501info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/20.500.14352/8075reponame:Docta Complutenseinstname:Universidad Complutense de Madrid (UCM)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Atribución-NoComercial-SinDerivadas 3.0 Españahttps://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:docta.ucm.es:20.500.14352/80752026-06-02T12:44:21Z
dc.title.none.fl_str_mv Money laundering and terrorism financing detection using neural networks and an abnormality indicator
title Money laundering and terrorism financing detection using neural networks and an abnormality indicator
spellingShingle Money laundering and terrorism financing detection using neural networks and an abnormality indicator
Rocha Salazar, José de Jesús
Money laundering
Financing of terrorism
Unsupervised learning
Detection
Machine Learning.
Dinero
5304.06 Dinero y Operaciones Bancarias
title_short Money laundering and terrorism financing detection using neural networks and an abnormality indicator
title_full Money laundering and terrorism financing detection using neural networks and an abnormality indicator
title_fullStr Money laundering and terrorism financing detection using neural networks and an abnormality indicator
title_full_unstemmed Money laundering and terrorism financing detection using neural networks and an abnormality indicator
title_sort Money laundering and terrorism financing detection using neural networks and an abnormality indicator
dc.creator.none.fl_str_mv Rocha Salazar, José de Jesús
Segovia Vargas, María Jesús
Camacho Miñano, Juana María Del Mar
author Rocha Salazar, José de Jesús
author_facet Rocha Salazar, José de Jesús
Segovia Vargas, María Jesús
Camacho Miñano, Juana María Del Mar
author_role author
author2 Segovia Vargas, María Jesús
Camacho Miñano, Juana María Del Mar
author2_role author
author
dc.contributor.none.fl_str_mv Universidad Complutense de Madrid
dc.subject.none.fl_str_mv Money laundering
Financing of terrorism
Unsupervised learning
Detection
Machine Learning.
Dinero
5304.06 Dinero y Operaciones Bancarias
topic Money laundering
Financing of terrorism
Unsupervised learning
Detection
Machine Learning.
Dinero
5304.06 Dinero y Operaciones Bancarias
description This study proposes a comprehensive model that helps improve self-comparisons and group-comparisons for customers to detect suspicious transactions related to money laundering (ML) and terrorism financing (FT) in financial systems. The self-comparison is improved by establishing a more comprehensive know your customer (KYC) policy, adding non-transactional characteristics to obtain a set of variables that can be classified into four categories: inherent, product, transactional, and geographic. The group-comparison involving the clustering process is improved by using an innovative transaction abnormality indicator, based on the variance of the variables. To illustrate the way this methodology works, random samples were extracted from the data warehouse of an important financial institution in Mexico. To train the algorithms, 26,751 and 3527 transactions and their features, involving natural and legal persons, respectively, were selected randomly from January 2020. To measure the prediction accuracy, test sets of 1000 and 600 transactions were selected randomly for natural and legal persons, respectively, from February 2020. The proposed model manages to decrease the proportion of false positives and increase accuracy when compared to the rule-based system. On reducing the false positive rate, the company’s costs for investigating suspicious customers also decrease significantly.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021-01-01
2021
2021-01-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14352/8075
url https://hdl.handle.net/20.500.14352/8075
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Atribución-NoComercial-SinDerivadas 3.0 España
https://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Atribución-NoComercial-SinDerivadas 3.0 España
https://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Docta Complutense
instname:Universidad Complutense de Madrid (UCM)
instname_str Universidad Complutense de Madrid (UCM)
reponame_str Docta Complutense
collection Docta Complutense
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.301603

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