Leite, Roger AlmeidaGschwandtner, TheresiaMiksch, SilviaGstrein, ErichKuntner, JohannesTobias Isenberg and Filip Sadlo2016-06-092016-06-092016978-3-03868-015-4-https://doi.org/10.2312/eurp.20161138https://diglib.eg.org:443/handle/10Financial institutions are always interested in ensuring security and quality for their customers. Banks, for instance, need to identify and avoid harmful transactions. In order to detect fraudulent operations, data mining techniques based on customer profile generation and verification are commonly used. However, these approaches are not supported by Visual Analytics techniques yet. We propose a Visual Analytics approach for supporting and fine-tuning profile analysis and reducing false positive alarms.Visual Knowledge DiscoveryTime Series DataBusiness and Finance VisualizationFinancial Fraud Detection.10.2312/eurp.2016113845-47