Using relational knowledge discovery to prevent securities fraud

Authors:
Jennifer Neville;Özgür Şimşek;David Jensen;John Komoroske;Kelly Palmer;Henry Goldberg
Affiliations:
University of Massachusetts - Amherst, Amherst, MA;University of Massachusetts - Amherst, Amherst, MA;University of Massachusetts - Amherst, Amherst, MA;National Association of Securities Dealers, Washington, DC;National Association of Securities Dealers, Washington, DC;National Association of Securities Dealers, Washington, DC
Venue:
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Year:
2005

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Abstract

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 private-sector 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 have not been easily used before now. 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 staff. Model predictions were found to correlate highly with the subjective evaluations of experienced NASD examiners. Furthermore, in all performance measures, our models performed as well as or better than the handcrafted rules that are currently in use at NASD.