GOTCHA! Network-Based Fraud Detection for Social Security Fraud

• 成果类型: Article
• 署名作者: Van Vlasselaer, Veronique; Eliassi-Rad, Tina; Akoglu, Leman; Snoeck, Monique; Baesens, Bart
• 署名单位: KU Leuven; Northeastern University; State University of New York (SUNY) System; Stony Brook University; University of Southampton
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2016.2489
• 发表日期: 2017
• 页码: 3090-3110
• 关键词: fraud detection; Network analysis; bipartite graphs; fraud propagation; guilt by association

摘要

We study the impact of network information for social security fraud detection. In a social security system, companies have to pay taxes to the government. This study aims to identify those companies that intentionally go bankrupt to avoid contributing their taxes. We link companies to each other through their shared resources, because some resources are the instigators of fraud. We introduce GOTCHA!, a new approach to define and extract features from a time-weighted network and to exploit and integrate network-based and intrinsic features in fraud detection. The GOTCHA! propagation algorithm diffuses fraud through the network, labeling the unknown and anticipating future fraud while simultaneously decaying the importance of past fraud. We find that domaindriven network variables have a significant impact on detecting past and future frauds and improve the baseline by detecting up to 55% additional fraudsters over time.