Securities Fraud Prevention Program

The Knowledge Discovery Laboratory (KDL) is a research group in the School of Computer Science. KDL has developed statistical methods that help the National Association of Securities Dealers (NASD) identify securities brokers who are likely to commit fraud. Data from public records are analyzed for social, professional, and organizational attributes and connections that may indicate possible fraudulent activity. These methods helps field examiners rank brokers by their propensity for fraud by using sophisticated statistical models learned directly from data.

Program Type: 

Impact Area: 

Program Impact Region: 

Program Participation: 

Neither applies

Program Start (and End Dates): 

August 1, 2005

Partner(s): 

National Association of Securities Dealers
1735 K Street NW
Washington, DC 20006
United States

Program Director: 

David Jensen

Program Director's Email Address: 

Program Contact: 

David Jensen

Contact Email: 

UNIT: Colleges, Schools, Departments, Centers, and Institutes: 

College of Natural Sciences/Computer Science

Organizational elements: 

Student Involvement: 

Yes

This Project Receives External Funding: 

Yes

Assessment Mechanism: 

No
Scholarly Product Type: 
Report

Scholarly Product Detail: 

J. Neville, Ö. Simsek, D. Jensen, J. Komoroske, K. Palmer, and H. Goldberg (2005). Using relational knowledge discovery to prevent securities fraud. Proc. of the 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
L. Friedland and D. Jensen. (2007). Finding tribes: Identifying close-knit individuals from employment patterns. Proc. of The 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
A. Fast, L. Friedland, M. Maier, B. Taylor, D. Jensen, H. Goldberg, & J. Komoroske (2007). Relational data pre-processing techniques for improved securities fraud detection. Proc. of The 13th ACM SIGKDD Intern. Conf. on Knowledge Discovery and Data Mining