ML Anthology

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

Yanchang Zhao, Huaifeng Zhang, Shanshan Wu, Jian Pei, Longbing Cao, Chengqi Zhang, Hans Bohlscheid
ECML-PKDD 2009 pp. 648-663
doi:10.1007/978-3-642-04174-7_42 /ecmlpkdd/2009/zhao2009ecmlpkdd-debt/

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.

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Cite

Text

Zhao et al. "Debt Detection in Social Security by Sequence Classification Using Both Positive and Negative Patterns." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2009. doi:10.1007/978-3-642-04174-7_42

Markdown

[Zhao et al. "Debt Detection in Social Security by Sequence Classification Using Both Positive and Negative Patterns." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2009.](https://mlanthology.org/ecmlpkdd/2009/zhao2009ecmlpkdd-debt/) doi:10.1007/978-3-642-04174-7_42

BibTeX

@inproceedings{zhao2009ecmlpkdd-debt,
  title     = {{Debt Detection in Social Security by Sequence Classification Using Both Positive and Negative Patterns}},
  author    = {Zhao, Yanchang and Zhang, Huaifeng and Wu, Shanshan and Pei, Jian and Cao, Longbing and Zhang, Chengqi and Bohlscheid, Hans},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2009},
  pages     = {648-663},
  doi       = {10.1007/978-3-642-04174-7_42},
  url       = {https://mlanthology.org/ecmlpkdd/2009/zhao2009ecmlpkdd-debt/}
}