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.
This work was supported by the Australian Research Council (ARC) Linkage Project LP0775041 and Early Career Researcher Grant from University of Technology, Sydney, Australia.
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Wu, S., Zhao, Y., Zhang, H., Zhang, C., Cao, L., Bohlscheid, H. (2009). Debt Detection in Social Security by Adaptive Sequence Classification. In: Karagiannis, D., Jin, Z. (eds) Knowledge Science, Engineering and Management. KSEM 2009. Lecture Notes in Computer Science(), vol 5914. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10488-6_21
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DOI: https://doi.org/10.1007/978-3-642-10488-6_21
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