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|Baesens Bart, Van Vlasselaer Vronique, Eliassi-Rad Tina, Akoglu Leman, Snoeck Monique
|security, computers: information, behaviour, networks, artificial intelligence
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.