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• W1569786601 abstract "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." @default.
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• W1569786601 date "2009-01-01" @default.
• W1569786601 modified "2023-10-17" @default.
• W1569786601 title "Debt Detection in Social Security by Sequence Classification Using Both Positive and Negative Patterns" @default.
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• W1569786601 doi "https://doi.org/10.1007/978-3-642-04174-7_42" @default.
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