Debt Detection in Social Security by Adaptive Sequence Classification

Authors:
Shanshan Wu;Yanchang Zhao;Huaifeng Zhang;Chengqi Zhang;Longbing Cao;Hans Bohlscheid
Affiliations:
Centre for Quantum Computation and Intelligent Systems (QCIS), University of Technology, Sydney, Australia;Centre for Quantum Computation and Intelligent Systems (QCIS), University of Technology, Sydney, Australia;Data Mining Section, Payment Reviews Branch, Business Integrity Division, Centrelink, Australia;Centre for Quantum Computation and Intelligent Systems (QCIS), University of Technology, Sydney, Australia;Centre for Quantum Computation and Intelligent Systems (QCIS), University of Technology, Sydney, Australia;Data Mining Section, Payment Reviews Branch, Business Integrity Division, Centrelink, Australia
Venue:
KSEM '09 Proceedings of the 3rd International Conference on Knowledge Science, Engineering and Management
Year:
2009

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Abstract

Debt detection is important for improving payment accuracy in social security. Since debt detection from customer transaction data can be generally modelled as a fraud detection problem, a straightforward solution is to extract features from transaction sequences and build a sequence classifier for debts. For long-running debt detections, the patterns in the transaction sequences may exhibit variation from time to time, which makes it imperative to adapt classification to the pattern variation. In this paper, we present a novel adaptive sequence classification framework for debt detection in a social security application. The central technique is to catch up with the pattern variation by boosting discriminative patterns and depressing less discriminative ones according to the latest sequence data.