Using relational knowledge discovery to prevent securities fraud

We describe an application of relational knowledge discovery to a key regulatory mission of the National Association of Securities Dealers (NASD). NASD is the world's largest privatesector securities regulator, with responsibility for preventing and discovering misconduct among securities brokers. Our goal was to help focus NASD's limited regulatory resources on the brokers who are most likely to engage in securities violations. Using statistical relational learning algorithms, we developed models that rank brokers with respect to the probability that they would commit a serious violation of securities regulations in the near future. Our models incorporate organizational relationships among brokers (e.g., past coworker), which domain experts consider important but cannot easily use otherwise. The learned models were subjected to an extensive evaluation using more than 18 months of data unseen by the model developers and comprising over two person weeks of effort by NASD staf...

• Type: Conference
• Year: 2005
• Where: KDD
• Authors: Özgür Simsek, David Jensen, Henry G. Goldberg, Jennifer Neville, John Komoroske, Kelly Palmer
• Keywords: Data Mining, Experienced Nasd Examiners, KDD 2005, NASD Staff, Securities Brokers