GOTCHA! Network-Based Fraud Detection for Social Security Fraud

Véronique Van Vlasselaer (), Tina Eliassi-Rad (), Leman Akoglu (), Monique Snoeck () and Bart Baesens ()
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Véronique Van Vlasselaer: Department of Decision Sciences and Information Management, KU Leuven, 3000 Leuven, Belgium
Tina Eliassi-Rad: Network Science Institute, College of Computer and Information Science, Northeastern University, Boston, Massachusetts 02115
Leman Akoglu: Department of Computer Science, Stony Brook University, Stony Brook, New York 11794
Monique Snoeck: Department of Decision Sciences and Information Management, KU Leuven, 3000 Leuven, Belgium
Bart Baesens: Department of Decision Sciences and Information Management, KU Leuven, 3000 Leuven, Belgium; and School of Management, University of Southampton, Highfield, Southampton SO17 1BJ, United Kingdom

Management Science, 2017, vol. 63, issue 9, 3090-3110

Abstract: 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 domain-driven 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.

Keywords: fraud detection; network analysis; bipartite graphs; fraud propagation; guilt by association (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (18)

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