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

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Relational data pre-processing techniques for improved securities fraud detection

Content Provider	ACM Digital Library
Author	Fast, Andrew Jensen, David Friedland, Lisa Goldberg, Henry G. Komoroske, John Taylor, Brian Maier, Marc
Abstract	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.
Starting Page	941
Ending Page	949
Page Count	9
File Format	PDF
ISBN	9781595936097
DOI	10.1145/1281192.1281293
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2007-08-12
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Statistical relational learning Normalization Data pre-processing Fraud detection Relational probability trees
Content Type	Text
Resource Type	Article

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