Network Analysis for Financial Fraud Detection

Security and quality are main concerns for private and public financial institutions. Data mining techniques based on the profiles of customers of a financial institution are commonly used to avoid fraud and financial damage. However, these approaches often are limited to the analysis of individual customers which hinders the detection of fraudulent networks. We propose a Visual Analytics approach for supporting and fine-tuning customers' network analysis, thus, reducing false-negative alarms of frauds.