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GOTCHA! Network-Based Fraud Detection for Social Security Fraud

Author

Listed:
  • 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)

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.

Suggested Citation

  • Véronique Van Vlasselaer & Tina Eliassi-Rad & Leman Akoglu & Monique Snoeck & Bart Baesens, 2017. "GOTCHA! Network-Based Fraud Detection for Social Security Fraud," Management Science, INFORMS, vol. 63(9), pages 3090-3110, September.
    • Handle: RePEc:inm:ormnsc:v:63:y:2017:i:9:p:3090-3110
    DOI: 10.1287/mnsc.2016.2489
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    References listed on IDEAS

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