Author
Jennifer Neville, John Komoroske, Kelly Palmer, David Jensen
Abstract
We describe an application of relational knowledge discov-
ery to a key regulatory mission of the National Associa-
tion of Securities Dealers (NASD). NASD is the world’s
largest private-sector securities regulator, with responsibil-
ity for preventing and discovering misconduct among secu-
rities 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 sub jected 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 sub jective 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.
