A Visualization Approach for Frauds Detection in Financial Market

The traditional solutions to the stock market security are not sufficient in identifying attackers and further attack plans from the analysis of existing events.Therefore, it is difficult for analysts to prevent future unexpected events or frauds by only monitoring the realtime trading information. The event-driven fraud detection in financial market could not help analysts to find attack plans and the further intention of attackers. This paper proposed a new framework of visual analytics for stock market security. The proposed solution consists of two stages: 1) visual surveillance of market performance, and 2) behavior-driven visual analysis of trading networks.In the first stage, we use a 3D treemaps to monitor the real-time stock market performance and to identify a particular stock that produced an unusual trading pattern. We then move to the next stage: social network visualization to conduct behavior-driven visual analysis of suspected pattern. Through the visual analysis of social (or trading)network, analysts may finally identify the attackers (the sources of the fraud), and further attack plans.