On the communal analysis suspicion scoring for identity crime in streaming credit applications
This paper describes a rapid technique: communal analysis suspicion scoring (CASS), for generating numeric suspicion scores on streaming credit applications based on implicit links to each other, over both time and space. CASS includes pair-wise communal scoring of identifier attributes for applications, definition of categories of suspiciousness for application-pairs, the incorporation of temporal and spatial weights, and smoothed k-wise scoring of multiple linked application-pairs. Results on mining several hundred thousand real credit applications demonstrate that CASS reduces false alarm rates while maintaining reasonable hit rates. CASS is scalable for this large data sample, and can rapidly detect early symptoms of identity crime. In addition, new insights have been observed from the relationships between applications.
communal scoring risk analysis credit application fraud detection multi-attribute directed graph dynamic application data streams anomaly detection