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GOTCHA! Network-based fraud detection for security fraud

GOTCHA! Network-based fraud detection for security fraud
GOTCHA! Network-based fraud detection for security fraud
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.
0025-1909
3090-3110
Van Vlasselaer, Veronique
80a16e2b-f1d0-4d27-bec5-fe3669d9477c
Eliassi-Rad, Tina
f7b2b811-0cbc-43d6-8005-49570f41018b
Akoglu, Leman
68ea2210-3c84-4bc5-b56d-7b72d46f5029
Snoeck, Monique
9aee96bc-8a57-4c37-bcd7-e83f0b173ee1
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Van Vlasselaer, Veronique
80a16e2b-f1d0-4d27-bec5-fe3669d9477c
Eliassi-Rad, Tina
f7b2b811-0cbc-43d6-8005-49570f41018b
Akoglu, Leman
68ea2210-3c84-4bc5-b56d-7b72d46f5029
Snoeck, Monique
9aee96bc-8a57-4c37-bcd7-e83f0b173ee1
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0

Van Vlasselaer, Veronique, Eliassi-Rad, Tina, Akoglu, Leman, Snoeck, Monique and Baesens, Bart (2017) GOTCHA! Network-based fraud detection for security fraud. Management Science, 63 (9), 3090-3110. (doi:10.1287/mnsc.2016.2489).

Record type: Article

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.

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Gotcha (final) MS - Accepted Manuscript
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Accepted/In Press date: 11 February 2016
e-pub ahead of print date: 14 July 2016
Published date: September 2017
Organisations: Decision Analytics & Risk, Southampton Business School

Identifiers

Local EPrints ID: 411040
URI: https://eprints.soton.ac.uk/id/eprint/411040
ISSN: 0025-1909
PURE UUID: 93c491c9-0e27-414c-8fe1-afe84ba7c381
ORCID for Bart Baesens: ORCID iD orcid.org/0000-0002-5831-5668

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Date deposited: 13 Jun 2017 16:32
Last modified: 17 Sep 2019 04:50

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Contributors

Author: Veronique Van Vlasselaer
Author: Tina Eliassi-Rad
Author: Leman Akoglu
Author: Monique Snoeck
Author: Bart Baesens ORCID iD

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