Adaptive communal detection in search of adversarial identity crime

Authors:
Clifton Phua;Vincent Lee;Kate Smith-Miles;Ross Gayler
Affiliations:
Monash University, Clayton, Victoria, Australia;Monash University, Clayton, Victoria, Australia;Deakin University, Burwood, Victoria, Australia;Baycorp Advantage, Melbourne, VIC, Australia
Venue:
Proceedings of the 2007 international workshop on Domain driven data mining
Year:
2007

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Abstract

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.