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Zeitschriftenartikel:

R. Leite, T. Gschwandtner, S. Miksch, Simone Kriglstein, M. Pohl, E. Gstrein, J. Kuntner:
"EVA: Visual Analytics to Identify Fraudulent Events";
IEEE Transactions on Visualization and Computer Graphics, 24 (2018), 1; S. 330 - 339.



Kurzfassung englisch:
Financial institutions are interested in ensuring security and quality for their customers. Banks, for instance, need to identify and stop harmful transactions in a timely manner. In order to detect fraudulent operations, data mining techniques and customer profile analysis are commonly used. However, these approaches are not supported by Visual Analytics techniques yet. Visual Analytics techniques have potential to considerably enhance the knowledge discovery process and increase the detection and prediction accuracy of financial fraud detection systems. Thus, we propose EVA, a Visual Analytics approach for supporting fraud investigation, fine-tuning fraud detection algorithms, and thus, reducing false positive alarms.

Schlagworte:
Data visualization, Complexity theory, Visual analytics, Data mining, Event detection


"Offizielle" elektronische Version der Publikation (entsprechend ihrem Digital Object Identifier - DOI)
http://dx.doi.org/10.1109/TVCG.2017.2744758

Elektronische Version der Publikation:
http://publik.tuwien.ac.at/files/publik_262142.pdf



Zugeordnete Projekte:
Projektleitung Silvia Miksch:
CVAST: Centre for Visual Analytics Science and Technology (Laura Bassi Centre of Expertise)


Erstellt aus der Publikationsdatenbank der Technischen Universitšt Wien.