NEVA: Visual Analytics to Identify Fraudulent Networks
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Date
2020
Journal Title
Journal ISSN
Volume Title
Publisher
© 2020 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd
Abstract
Trust‐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.
Description
@article{10.1111:cgf.14042,
journal = {Computer Graphics Forum},
title = {{NEVA: Visual Analytics to Identify Fraudulent Networks}},
author = {A. Leite, Roger and Gschwandtner, Theresia and Miksch, Silvia and Gstrein, Erich and Kuntner, Johannes},
year = {2020},
publisher = {© 2020 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd},
ISSN = {1467-8659},
DOI = {10.1111/cgf.14042}
}