Debt Detection in Social Security by Sequence Classification Using Both Positive and Negative Patterns

Publisher:
Springer
Publication Type:
Conference Proceeding
Citation:
Machine Learning and Knowledge Discovery in Databases, European Conference, ECML PKDD 2009, 2009, pp. 648 - 663
Issue Date:
2009-01
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Debt detection is important for improving payment accuracy in social security. Since debt detection from customer transactional 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. The existing sequence classification methods based on sequential patterns consider only positive patterns. However, according to our experience in a large social security application, negative patterns are very useful in accurate debt detection. In this paper, we present a successful case study of debt detection in a large social security application. The central technique is building sequence classification using both positive and negative sequential patterns.
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