EVA: Visual Analytics to Identify Fraudulent Events
Roger A. Leite, Theresia Gschwandtner, Silvia Miksch, Simone Kriglstein, Margit Pohl, Erich Gstrein, Johannes Kuntner · IEEE Transactions on Visualization and Computer Graphics · 2017
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.