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The Challenge of Quality Evaluation in Fraud Detection

hal.structure.identifierLab-STICC_IMTA_CID_DECIDE
hal.structure.identifierDépartement lmage et Traitement Information [IMT Atlantique - ITI]
dc.contributor.authorPUENTES, John
hal.structure.identifierLab-STICC_IMTA_CID_DECIDE
hal.structure.identifierChaire cyberdéfense systèmes navals (Ecole Navale, IMT-Atlantique, THALES, DCNS)
dc.contributor.authorMERINO LASO, Pedro
hal.structure.identifierInstitut de Recherche de l'Ecole Navale [IRENAV]
dc.contributor.authorBROSSET, David
dc.date.accessioned2021-05-14T09:46:13Z
dc.date.available2021-05-14T09:46:13Z
dc.date.issued2018-09
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/76985
dc.description.abstractEnA system is considered to be safety-critical when its failure may lead to unacceptable consequences [17]. One failure of particular interest in safety-critical systems is fraud, understood as abusing a position of trust to get illegal advantage and/or cause losses by false suggestions or suppression of the truth. Although some safety-critical systems are protected by design against known threats, innovative exploitation of latent vulnerabilities remains a possibility. Fraud has multiple consequences on provision of services, management of organizations, and operational infrastructure, causing damage to human lives, production processes, and the natural environment. Paradoxically, in fraud situations, compliance to fitness for use seems to be appropriate, while conformity to procedures is cleverly bypassed. Also, even if automatic procedures continuously check for potential fraud, legitimate users may commit fraud inside organizations or maliciously manipulated applications can have significant unknown repercussions on a system. Besides, voluminous heterogeneous data and information are exchanged in and between systems. For these reasons, quality evaluation in fraud situations is very complex. The automobile industry [14], banks [7], health institutions [21], and government [2] are some examples of organizations affected by frauds. Taking into account that the quality assessment of procedures and processes is still an open problem [1] [5], this proposal focuses on data and information quality within those procedures. This article discusses why quality assessment in fraud contexts is a challenging problem and argues for a longitudinal quality meta-analysis, relying on contextual cumulative indicators.
dc.language.isoen
dc.publisherACM
dc.subject.enData analytics
dc.subject.enQuality meta-analysis
dc.subject.enContext
dc.subject.enCumulative indicators
dc.subject.enFraud life cycle
dc.title.enThe Challenge of Quality Evaluation in Fraud Detection
dc.typeArticle de revue
dc.identifier.doi10.1145/3228341
dc.subject.halSciences de l'ingénieur [physics]/Traitement du signal et de l'image
dc.subject.halInformatique [cs]/Autre [cs.OH]
bordeaux.journalJournal of data and information quality
bordeaux.page5:1 - 5:4
bordeaux.volume10
bordeaux.hal.laboratoriesInstitut de Mécanique et d’Ingénierie de Bordeaux (I2M) - UMR 5295*
bordeaux.issue2
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.institutionINRAE
bordeaux.institutionArts et Métiers
bordeaux.peerReviewedoui
hal.identifierhal-01890754
hal.version1
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-01890754v1
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