document: Relational data pre-processing techniques for improved securities fraud detection 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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