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By Clifton Phua, Member, IEEE, Kate Smith-Miles, Senior Member, IEEE, Vincent Lee, and Ross Gayler
Abstract
Identity crime is well known, prevalent, and costly; and credit application fraud is a specific case of identity crime.
The existing non-data mining detection systems of business rules and scorecards, and known fraud matching have limitations. To address
these limitations and combat identity crime in real-time, this paper proposes a new multi-layered detection system complemented with
two additional layers: Communal Detection (CD) and Spike Detection (SD). CD finds real social relationships to reduce the suspicion
score, and is tamper-resistant to synthetic social relationships. It is the whitelist-oriented approach on a fixed set of attributes. SD
finds spikes in duplicates to increase the suspicion score, and is probe-resistant for attributes. It is the attribute-oriented approach on
a variable-size set of attributes.
Together, CD and SD can detect more types of attacks, better account for changing legal behaviour, and remove the redundant attributes. Experiments were carried out on CD and SD with several million real credit applications. Results on the data support the hypothesis that successful credit application fraud patterns are sudden and exhibit sharp spikes in duplicates. Although this research is specific to credit application fraud detection, the concept of resilience, together with adaptivity and quality data discussed in the paper, are general to the design, implementation, and evaluation of all detection systems.
Index Terms data mining-based fraud detection, security, data stream mining, anomaly detection.
Here is Resilient Identity Crime Detection paper (PDF), to be published in IEEE Transactions On Knowledge and Data Engineering, 2011.