Commercial datasets are often large, relational, and dynamic. They contain many records of people, places, things, events and their interactions over time. Such datasets are rarely structured appropriately for knowledge discovery, and they often contain variables whose meanings change across different subsets of the data. We describe how these challenges were addressed in a collaborative analysis project undertaken by the University of Massachusetts Amherst and the National Association of Securities Dealers(NASD). We describe several methods for data pre-processing that we applied to transform a large, dynamic, and relational dataset describing nearly the entirety of the U.S. securities industry, and we show how these methods made the dataset suitable for learning statistical relational models. To better utilize social structure, we first applied known consolidation and link formation techniques to associate individuals with branch office locations. In addition, we developed an innovative technique to infer professional associations by exploiting dynamic employment histories. Finally, we applied normalization techniques to create a suitable class label that adjusts for spatial, temporal, and other heterogeneity within the data. We show how these pre-processing techniques combine to provide the necessary foundation for learning high-performing statistical models of fraudulent activity.
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