Publications in Scientific Journals:

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; 330 - 339.

English abstract:
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.

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

"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)

Electronic version of the publication:

Related Projects:
Project Head Silvia Miksch:
CVAST: Centre for Visual Analytics Science and Technology (Laura Bassi Centre of Expertise)

Created from the Publication Database of the Vienna University of Technology.