SNARE: a link analytic system for graph labeling and risk detection

Classifying nodes in networks is a task with a wide range of applications. It can be particularly useful in anomaly and fraud detection. Many resources are invested in the task of fraud detection due to the high cost of fraud, and being able to automatically detect potential fraud quickly and precisely allows human investigators to work more efficiently. Many data analytic schemes have been put into use; however, schemes that bolster link analysis prove promising. This work builds upon the belief propagation algorithm for use in detecting collusion and other fraud schemes. We propose an algorithm called SNARE (Social Network Analysis for Risk Evaluation). By allowing one to use domain knowledge as well as link knowledge, the method was very successful for pinpointing misstated accounts in our sample of general ledger data, with a significant improvement over the default heuristic in true positive rates, and a lift factor of up to 6.5 (more than twice that of the default heuristic). We...

• Type: Conference
• Year: 2009
• Where: KDD
• Authors: Mary McGlohon, Stephen Bay, Markus G. Anderle, David M. Steier, Christos Faloutsos
• Keywords: Algorithm Called Snare, Data Mining, Fraud Detection, Fraud Schemes, KDD 2009