An Outlier Detection Model Based on Cross Datasets Comparison for Financial Surveillance

摘要：

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 and further 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.

关键词： Finance Surveillance Outlier Detection Behaviour Pattern Recognition Knowledge Discovery

作者: ZHU TIANQING

作者单位: DEPARTMENT OF COMPUTER INFORMATION ENGINEERING, WUHAN POLYTECHNIC UNIVERSITY, Wuhan 430023, P.R. China

会议类型: 国际会议

会议名称: 2006 Asia-Pacific Services Computing Conference(IEEE亚太地区服务计算会议)

会议地点: 广州

会议语种:英文

页码: 601-604

在线出版日期: 2006-12-12（万方平台首次上网日期，不代表论文的发表时间）