Name
Title | ||
|---|---|---|
Debt Detection in Social Security by Sequence Classification Using Both Positive and Negative Patterns |
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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-04174-7_42 | ECML/PKDD |
Keywords | Field | DocType |
fraud detection problem,transaction sequence,debt detection,negative patterns,negative pattern,sequence classifier,accurate debt detection,sequence classification,existing sequence classification method,social security,large social security application,transaction data | Data mining,Computer science,Debt,Social security,Database transaction,Classifier (linguistics),Transaction data,Payment | Conference |
Volume | ISSN | Citations |
5782 | 0302-9743 | 5 |
PageRank | References | Authors |
0.44 | 27 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yanchang Zhao | 1 | 233 | 20.01 |
| Huaifeng Zhang | 2 | 240 | 18.84 |
| Shanshan Wu | 3 | 106 | 16.37 |
| Jian Pei | 4 | 19002 | 995.54 |
| Longbing Cao | 5 | 2212 | 185.04 |
| Chengqi Zhang | 6 | 3636 | 274.41 |
| Hans Bohlscheid | 7 | 40 | 3.71 |