Network visualization for financial crime detection

Objective: We present a new software system, VisFAN, for the visual analysis of financial activity networks.MethodsWe combine enhanced graph drawing techniques to devise novel algorithms and interaction functionalities for the visual exploration of networked data sets, together with tools for SNA and for the automatic generation of reports.ResultsAn application example constructed on real data is presented. We also report the results of a study aimed at qualitatively understanding the satisfaction level of the analysts when using VisFAN.ConclusionVisFAN makes a strong use of visual interactive tools, combined with ad-hoc clustering techniques and customizable layout constraints management.ImplicationsAs this system confirms, information visualization can play a crucial role to face the discovery of financial crimes.