Sequence Matching for Suspicious Activity Detection in Anti-Money Laundering
PAISI, PACCF and SOCO '08 Proceedings of the IEEE ISI 2008 PAISI, PACCF, and SOCO international workshops on Intelligence and Security Informatics
Improving the performance of fuzzy clustering algorithms through outlier identification
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
| Hi-index | 0.00 |
Outlier detection is a key element for intelligent financial surveillance systems which intend to identify fraud and money laundering by discovering unusual customer behaviour pattern. The detection procedures generally fall into two categories: comparing every transaction against its account history andficrther more, comparing against a peer group to determine if the behavior is unusual. The later approach shows particular merits in efficiently extracting suspicious transaction and reducing false positive rate. Peer group analysis concept is largely dependent on a cross-datasets outlier detection model. In this paper, we propose a new cross outlier detection model based on distance definition incorporated with the financial transaction data features. An approximation algorithm accompanied with the model is provided to optimize the computation of the deviation from tested data point to the reference dataset. An experiment based on real bank data blended with synthetic outlier cases shows promising results of our model in reducing false positive rate while enhancing the discriminative rate remarkably.