| dc.contributor.author | Leite, Roger Almeida | en_US |
| dc.contributor.author | Gschwandtner, Theresia | en_US |
| dc.contributor.author | Miksch, Silvia | en_US |
| dc.contributor.author | Gstrein, Erich | en_US |
| dc.contributor.author | Kuntner, Johannes | en_US |
| dc.contributor.editor | Tobias Isenberg and Filip Sadlo | en_US |
| dc.date.accessioned | 2016-06-09T09:33:32Z | |
| dc.date.available | 2016-06-09T09:33:32Z | |
| dc.date.issued | 2016 | en_US |
| dc.identifier.isbn | 978-3-03868-015-4 | en_US |
| dc.identifier.issn | - | en_US |
| dc.identifier.uri | http://dx.doi.org/10.2312/eurp.20161138 | en_US |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10 | |
| dc.description.abstract | Financial 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. | en_US |
| dc.publisher | The Eurographics Association | en_US |
| dc.subject | Visual Knowledge Discovery | en_US |
| dc.subject | Time Series Data | en_US |
| dc.subject | Business and Finance Visualization | en_US |
| dc.subject | Financial Fraud Detection. | en_US |
| dc.title | Visual Analytics for Fraud Detection: Focusing on Profile Analysis | en_US |
| dc.description.seriesinformation | EuroVis 2016 - Posters | en_US |
| dc.description.sectionheaders | Poster | en_US |
| dc.identifier.doi | 10.2312/eurp.20161138 | en_US |
| dc.identifier.pages | 45-47 | en_US |
