Adaptive communal detection in search of adversarial identity crime

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.

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Index Terms

• Applied computing

  • Enterprise computing

• Computing methodologies

  • Machine learning

    • Learning paradigms

    • Machine learning approaches

      • Markov decision processes

• Information systems

  • Information systems applications

    • Data mining

• Theory of computation

  • Theory and algorithms for application domains

    • Machine learning theory

      • Markov decision processes