| Financial crime is one of the most difficult issues we were facing today, including money laundering. The activity of money-laundering is a gnarled and complicated network. It is far more not enough to dig out the suspicious object and trace the suspicious money effectively in such activity networks without the domain knowledge. In order to solve this problem, we put forward a visualization analysis based anti-money laundering platform. It can display financial activity data through multiple view, and connection data and analysis through the interactive functions.Network diagram is on of the most abundant solution to visualize data. We put forward a general method to transform the financial activity into a network effectively after studied the characteristics and patterns of the financial activities. The account node can describe the customer entity from three levels in the financial activities. The attribute node describe the activity of accessing cash. The edge connect two node, describing the activity between two financial entities represented by the two node. The main problem of network visualization is layout. How to layout a network effectively impact directly on the performance of visual analysis. In order to demonstrate the pattern of money-laundering in financial network activities and improve the efficiency of visual analysis, we proposed a force based network layout algorithm GDAL(Gang Detecting Analysis Layout).GDAL layout the network in three steps. In step one, the GDAL cluster the nodes to a hierarchical structure with the knowledge of anti-money network's pattern and k core decomposition theory. Then GDAL pre-layout the network with the result of step one. In step tree, GDAL complete the layout with improved FR layout algorithm. The experimental results show that GDAL can be more intuitive to reveal the relationship between financial entities in the network. And GDAL has obvious improvement on the layout efficiency and quality when compared with FR layout algorithm. |
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