C4.5: programs for machine learning
C4.5: programs for machine learning
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Information awareness: a prospective technical assessment
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Link mining applications: progress and challenges
ACM SIGKDD Explorations Newsletter
| Hi-index | 0.00 |
Break detection systems are a subclass of KDD-based fraud detection applications in which the fraudulent activity is indicated by complex patterns of transactions, typically involving related entities performing multiple activities and playing several distinct roles over a time period, during which they may also be engaged in legitimate activities of the same types with the same or other entities. Break detection systems take as their input a large transaction stream and provide as their output a set of breaks, or leads, which are used to initiate follow-up investigations by trained human analysts. This article discusses two such systems: the FinCEN AI System (FAIS) and the Advanced Detection System (ADS). FAIS was developed for and is used by the U.S. Department of the Treasury's Financial Crimes Enforcement Network (FinCEN). The purpose of FAIS is to detect instances of potential money laundering from the database of reports of large cash transactions. ADS was developed for and is used by the National Association of Securities Dealers (NASD®) Regulation, Inc., the subsidiary of NASD responsible for regulation of the Nasdaq Stock Market. The purpose of ADS is to detect potential instances of violations of the rules of participation in the Nasdaq® and related stock markets subject to NASD Regulation's oversight and jurisdiction.