Most risky transactions identified by rule sets are sent into review queues.
Queued transactions are prioritized and routed to agents in specific domains.
Case review and investigation are conducted.</li></li></ul><li>Implementation Challenges<br />Problems<br />Realities<br />Fast-Growing International Footprint<br />Overwhelming Number of Segments & Models<br />Extremely Rich Data from Diversified Sources<br />Information Overload instead of Data Mining<br />Ever-Complicated IT Infrastructure<br />High Exposures to System Risks<br />Dynamic Fraud Trends & Smarter Fraudsters<br />Escalating Model Decay & Deterioration<br />
Data-Driven Model (DDM) Strategy<br />Dynamic Rule Induction<br />Automatic Model Development<br />Real-Time Deployment<br />Conceptual <br />DDM<br />Modular Data Processing<br />Daily Monitoring<br />Implemented by<br />Rapid Model Refresh (RMR)<br />
Exhaustive Search on 1-Dimension Space, e.g. Score
Induce 1-Level Binary Tree by Minimizing Gini Impurity
Use to Find the Best Score Cutoff while Balancing Review Rate</li></li></ul><li>Pick Winner from Multiple Candidates<br />Generically Support Arbitrary Number of Score Inputs for Massive Models Evaluation and Deployment<br />
RMR – Deployment Layer<br />Model Specifications<br />Perl<br />Convert to XML / PMML<br />Inject into Web Engine<br />Collect Web Logs in DB<br />SAS<br />Monitor Daily Scoring Stability<br />Shell<br />Email Reports to Stakeholders<br />
A Use Case: Score Monitoring<br />Objectives:<br /><ul><li>System Breakage