Effective detection of sophisticated online bankin

Authors: Wei Wei Jinjiu Li Longbing Cao Yuming Ou Jiahang Chen

Publish Date: 2012/07/19

Volume: 16, Issue: 4, Pages: 449-475

PDF Link

Abstract

Sophisticated online banking fraud reflects the integrative abuse of resources in social cyber and physical worlds Its detection is a typical use case of the broadbased Wisdom Web of Things W2T methodology However there is very limited information available to distinguish dynamic fraud from genuine customer behavior in such an extremely sparse and imbalanced data environment which makes the instant and effective detection become more and more important and challenging In this paper we propose an effective online banking fraud detection framework that synthesizes relevant resources and incorporates several advanced data mining techniques By building a contrast vector for each transaction based on its customer’s historical behavior sequence we profile the differentiating rate of each current transaction against the customer’s behavior preference A novel algorithm ContrastMiner is introduced to efficiently mine contrast patterns and distinguish fraudulent from genuine behavior followed by an effective pattern selection and risk scoring that combines predictions from different models Results from experiments on largescale real online banking data demonstrate that our system can achieve substantially higher accuracy and lower alert volume than the latest benchmarking fraud detection system incorporating domain knowledge and traditional fraud detection methods

Keywords: