NEVA: Visual Analytics to Identify Fraudulent Networks

dc.contributor.authorA. Leite, Rogeren_US
dc.contributor.authorGschwandtner, Theresiaen_US
dc.contributor.authorMiksch, Silviaen_US
dc.contributor.authorGstrein, Erichen_US
dc.contributor.authorKuntner, Johannesen_US
dc.contributor.editorBenes, Bedrich and Hauser, Helwigen_US
dc.date.accessioned2020-10-06T16:54:03Z
dc.date.available2020-10-06T16:54:03Z
dc.date.issued2020
dc.description.abstractTrust‐ability, reputation, security and quality are the main concerns for public and private financial institutions. To detect fraudulent behaviour, several techniques are applied pursuing different goals. For well‐defined problems, analytical methods are applicable to examine the history of customer transactions. However, fraudulent behaviour is constantly changing, which results in ill‐defined problems. Furthermore, analysing the behaviour of individual customers is not sufficient to detect more complex structures such as networks of fraudulent actors. We propose NEVA (Network dEtection with Visual Analytics), a Visual Analytics exploration environment to support the analysis of customer networks in order to reduce false‐negative and false‐positive alarms of frauds. Multiple coordinated views allow for exploring complex relations and dependencies of the data. A guidance‐enriched component for network pattern generation, detection and filtering support exploring and analysing the relationships of nodes on different levels of complexity. In six expert interviews, we illustrate the applicability and usability of NEVA.en_US
dc.description.number6
dc.description.sectionheadersArticles
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume39
dc.identifier.doi10.1111/cgf.14042
dc.identifier.issn1467-8659
dc.identifier.pages344-359
dc.identifier.urihttps://doi.org/10.1111/cgf.14042
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14042
dc.publisher© 2020 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltden_US
dc.subjectvisualization
dc.subjectvisual analytics
dc.subjectfinancial fraud detection
dc.titleNEVA: Visual Analytics to Identify Fraudulent Networksen_US
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