On the communal analysis suspicion scoring for identity crime in streaming credit applications

Phua, Clifton, Gayler, Ross, Lee, Vincent and Smith-Miles, Kate 2009, On the communal analysis suspicion scoring for identity crime in streaming credit applications, European journal of operational research, vol. 195, no. 2, pp. 595-612.

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Title On the communal analysis suspicion scoring for identity crime in streaming credit applications
Author(s) Phua, Clifton
Gayler, Ross
Lee, Vincent
Smith-Miles, Kate
Journal name European journal of operational research
Volume number 195
Issue number 2
Start page 595
End page 612
Publisher Elsevier BV
Place of publication Amsterdam, Netherlands
Publication date 2009-06-01
ISSN 0377-2217
1872-6860
Keyword(s) risk analysis
credit application fraud detection
communal scoring
multi-attribute directed graph
dynamic application data streams
anomaly detection
Summary 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.
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
HERDC Research category C1 Refereed article in a scholarly journal
HERDC collection year 2009
Copyright notice ©2008, Elsevier B.V.
Persistent URL http://hdl.handle.net/10536/DRO/DU:30022575

Document type: Journal Article
Collection: School of Information Technology
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