Vorträge und Posterpräsentationen (ohne Tagungsband-Eintrag):
R. Leite, T. Gschwandtner, S. Miksch, Simone Kriglstein, M. Pohl, E. Gstrein, J. Kuntner:
"EVA: Visual Analytics to Identify Fraudulent Events";
Vortrag: IEEE VIS Conference,
Phoenix, AZ, USA;
01.10.2017
- 06.10.2017.
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
Elektronische Version der Publikation:
http://publik.tuwien.ac.at/files/publik_262143.pdf
Erstellt aus der Publikationsdatenbank der Technischen Universität Wien.