Debt Detection in Social Security by Adaptive Sequence Classification

Debt detection is important for improving payment accuracy in social security. Since debt detection from customer transaction 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. For long-running debt detections, the patterns in the transaction sequences may exhibit variation from time to time, which makes it imperative to adapt classification to the pattern variation. In this paper, we present a novel adaptive sequence classification framework for debt detection in a social security application. The central technique is to catch up with the pattern variation by boosting discriminative patterns and depressing less discriminative ones according to the latest sequence data.

[1]  Dino Pedreschi,et al.  A classification-based methodology for planning audit strategies in fraud detection , 1999, KDD '99.

[2]  Jiawei Han,et al.  Frequent pattern mining: current status and future directions , 2007, Data Mining and Knowledge Discovery.

[3]  Tom Fawcett,et al.  Adaptive Fraud Detection , 1997, Data Mining and Knowledge Discovery.

[4]  Chengqi Zhang,et al.  Customer Activity Sequence Classification for Debt Prevention in Social Security , 2009, Journal of Computer Science and Technology.

[5]  Mohammed J. Zaki,et al.  Mining features for sequence classification , 1999, KDD '99.

[6]  Yizhak Idan,et al.  Discovery of fraud rules for telecommunications—challenges and solutions , 1999, KDD '99.

[7]  H. Dominic Covvey,et al.  Adaptive Fraud Detection Using Benford's Law , 2006, Canadian Conference on AI.

[8]  Jiawei Han,et al.  Discriminative Frequent Pattern Analysis for Effective Classification , 2007, 2007 IEEE 23rd International Conference on Data Engineering.

[9]  Salvatore J. Stolfo,et al.  Adaptive Intrusion Detection: A Data Mining Approach , 2000, Artificial Intelligence Review.

[10]  Dimitrios I. Fotiadis,et al.  A two-stage methodology for sequence classification based on sequential pattern mining and optimization , 2008, Data Knowl. Eng..

[11]  Vincent S. Tseng,et al.  CBS: A New Classification Method by Using Sequential Patterns , 2005, SDM.

[12]  D. Madigan,et al.  Proceedings : KDD-99 : the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 15-18, 1999, San Diego, California, USA , 1999 .

[13]  Qingzhong Liu,et al.  Tree Based Behavior Monitoring for Adaptive Fraud Detection , 2006, 18th International Conference on Pattern Recognition (ICPR'06).