Evaluation of deep neural networks for reduction of credit card fraud alerts

Fraud detection systems support advanced detection techniques based on complex rules, statistical modelling and machine learning. However, alerts triggered by these systems still require expert judgement to either confirm a fraud case or discard a false positive. Reducing the number of false positiv...

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Detalles Bibliográficos
Autores: San Miguel Carrasco, Rafael, Sicilia Urbán, Miguel Ángel|||0000-0003-3067-4180
Tipo de recurso: artículo
Fecha de publicación:2020
País:España
Institución:Universidad de Alcalá (UAH)
Repositorio:e_Buah Biblioteca Digital Universidad de Alcalá
Idioma:inglés
OAI Identifier:oai:ebuah.uah.es:10017/60624
Acceso en línea:http://hdl.handle.net/10017/60624
https://dx.doi.org/10.1109/ACCESS.2020.3026222
Access Level:acceso abierto
Palabra clave:Neural networks
Deep learning
Fraud detection
Alert reduction
Informática
Computer science
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spelling Evaluation of deep neural networks for reduction of credit card fraud alertsSan Miguel Carrasco, RafaelSicilia Urbán, Miguel Ángel|||0000-0003-3067-4180Neural networksDeep learningFraud detectionAlert reductionInformáticaComputer scienceFraud detection systems support advanced detection techniques based on complex rules, statistical modelling and machine learning. However, alerts triggered by these systems still require expert judgement to either confirm a fraud case or discard a false positive. Reducing the number of false positives that fraud analysts investigate, by automating their detection with computer-assisted techniques, can lead to significant cost efficiencies. Alert reduction has been achieved with different techniques in related fields like intrusion detection. Furthermore, deep learning has been used to accomplish this task in other fields. In our paper, a set of deep neural networks have been tested to measure their ability to detect false positives, by processing alerts triggered by a fraud detection system. The performance achieved by each neural network setting is presented and discussed. The optimal setting allowed to capture 91.79% of total fraud cases with 35.16% less alerts. Obtained alert reduction rate would entail a significant reduction in cost of human labor, because alerts classified as false positives by the neural network wouldn't require human inspection.IEEE20202020-09-23journal articlehttp://purl.org/coar/resource_type/c_6501NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10017/60624https://dx.doi.org/10.1109/ACCESS.2020.3026222reponame:e_Buah Biblioteca Digital Universidad de Alcaláinstname:Universidad de Alcalá (UAH)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-ShareAlike 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccessoai:ebuah.uah.es:10017/606242026-06-18T11:13:07Z
dc.title.none.fl_str_mv Evaluation of deep neural networks for reduction of credit card fraud alerts
title Evaluation of deep neural networks for reduction of credit card fraud alerts
spellingShingle Evaluation of deep neural networks for reduction of credit card fraud alerts
San Miguel Carrasco, Rafael
Neural networks
Deep learning
Fraud detection
Alert reduction
Informática
Computer science
title_short Evaluation of deep neural networks for reduction of credit card fraud alerts
title_full Evaluation of deep neural networks for reduction of credit card fraud alerts
title_fullStr Evaluation of deep neural networks for reduction of credit card fraud alerts
title_full_unstemmed Evaluation of deep neural networks for reduction of credit card fraud alerts
title_sort Evaluation of deep neural networks for reduction of credit card fraud alerts
dc.creator.none.fl_str_mv San Miguel Carrasco, Rafael
Sicilia Urbán, Miguel Ángel|||0000-0003-3067-4180
author San Miguel Carrasco, Rafael
author_facet San Miguel Carrasco, Rafael
Sicilia Urbán, Miguel Ángel|||0000-0003-3067-4180
author_role author
author2 Sicilia Urbán, Miguel Ángel|||0000-0003-3067-4180
author2_role author
dc.subject.none.fl_str_mv Neural networks
Deep learning
Fraud detection
Alert reduction
Informática
Computer science
topic Neural networks
Deep learning
Fraud detection
Alert reduction
Informática
Computer science
description Fraud detection systems support advanced detection techniques based on complex rules, statistical modelling and machine learning. However, alerts triggered by these systems still require expert judgement to either confirm a fraud case or discard a false positive. Reducing the number of false positives that fraud analysts investigate, by automating their detection with computer-assisted techniques, can lead to significant cost efficiencies. Alert reduction has been achieved with different techniques in related fields like intrusion detection. Furthermore, deep learning has been used to accomplish this task in other fields. In our paper, a set of deep neural networks have been tested to measure their ability to detect false positives, by processing alerts triggered by a fraud detection system. The performance achieved by each neural network setting is presented and discussed. The optimal setting allowed to capture 91.79% of total fraud cases with 35.16% less alerts. Obtained alert reduction rate would entail a significant reduction in cost of human labor, because alerts classified as false positives by the neural network wouldn't require human inspection.
publishDate 2020
dc.date.none.fl_str_mv 2020
2020-09-23
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10017/60624
https://dx.doi.org/10.1109/ACCESS.2020.3026222
url http://hdl.handle.net/10017/60624
https://dx.doi.org/10.1109/ACCESS.2020.3026222
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
Attribution-NonCommercial-ShareAlike 4.0 International
http://creativecommons.org/licenses/by-nc-sa/4.0/
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
Attribution-NonCommercial-ShareAlike 4.0 International
http://creativecommons.org/licenses/by-nc-sa/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv IEEE
publisher.none.fl_str_mv IEEE
dc.source.none.fl_str_mv reponame:e_Buah Biblioteca Digital Universidad de Alcalá
instname:Universidad de Alcalá (UAH)
instname_str Universidad de Alcalá (UAH)
reponame_str e_Buah Biblioteca Digital Universidad de Alcalá
collection e_Buah Biblioteca Digital Universidad de Alcalá
repository.name.fl_str_mv
repository.mail.fl_str_mv
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