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

Yanchang Zhao, Huaifeng Zhang, Shanshan Wu, Jian Pei, Longbing Cao, Chengqi Zhang, Hans Bohlscheid
2009 Lecture Notes in Computer Science  
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,
more » ... e 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.
doi:10.1007/978-3-642-04174-7_42 fatcat:xqmm4citfjfifo7mra6hambtwy