Adaptive communal detection in search of adversarial identity crime
conference contribution
posted on 2007-01-01, 00:00authored byC Phua, V Lee, K Smith-Miles, R Gayler
This paper is on adaptive real-time searching of credit application data streams for identity crime with many search parameters. Specifically, we concentrated on handling our domain-specific adversarial activity problem with the adaptive Communal Analysis Suspicion Scoring (CASS) algorithm. CASS's main novel theoretical contribution is in the formulation of State-of- Alert (SoA) which sets the condition of reduced, same, or heightened watchfulness; and Parameter-of-Change (PoC) which improves detection ability with pre-defined parameter values for each SoA. With pre-configured SoA policy and PoC strategy, CASS determines when, what, and how much to adapt its search parameters to ongoing adversarial activity. The above approach is validated with three sets of experiments, where each experiment is conducted on several million real credit applications and measured with three appropriate performance metrics. Significant improvements are achieved over previous work, with the discovery of some practical insights of adaptivity into our domain.
History
Event
International Workshop on Domain Driven Data Mining (2007 : San Jose, California)
Pagination
1 - 10
Publisher
ACM
Location
San Jose, California
Place of publication
New York, N.Y
Start date
2007-08-12
End date
2007-08-15
ISBN-13
9781595938466
ISBN-10
159593846X
Language
eng
Publication classification
E1 Full written paper - refereed
Copyright notice
2007 ACM
Editor/Contributor(s)
P Yu
Title of proceedings
Proceedings of the 2007 International Workshop on Domain Driven Data Mining : August 12, 2007, San Jose, California