Title: Gotch’all! Advanced network analysis for detecting groups of fraud
Authors: Van Vlasselaer, Véronique
Akoglu, Leman
Eliassi-Rad, Tina
Snoeck, Monique
Baesens, Bart
Issue Date: 2014
Conference: PAW (Predictive Analytics World) location:London (UK) date:29-30 October 2014
Abstract: The Belgian Social Security Institution is a federal agency that registers and monitors every active company in Belgium, and is responsible for the collection of employer and employee tax contributions. These contributions are collected at employer level, making this process highly sensitive to payment fraud. As traditional techniques fail to meet the complex requirements of fraud, advanced social network analysis (SNA) offers new insights in the propagation of fraud through a network. We observe that fraud is often not something an individual would commit by himself, but is organized by groups of people loosely connected to each other. In this presentation, we will discuss how network analysis can ameliorate detection models for (1) detecting suspicious individual behavior based on his/her relationships to others; and (2) uncovering the so-called webs of frauds, i.e. groups of people frequently associated with fraudulent activities. We apply and validate the performance of the techniques on a real-life data set provided by the Belgian Social Security Institution.
Publication status: published
KU Leuven publication type: IMa-p
Appears in Collections:Research Center for Management Informatics (LIRIS), Leuven

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