Publication
Title
Corporate residence fraud detection
Author
Abstract
With the globalisation of the world's economies and ever-evolving financial structures, fraud has become one of the main dissipaters of government wealth and perhaps even a major contributor in the slowing down of economies in general. Although corporate residence fraud is known to be a major factor, data availability and high sensitivity have caused this domain to be largely untouched by academia. The current Belgian government has pledged to tackle this issue at large by using a variety of in-house approaches and cooperations with institutions such as academia, the ultimate goal being a fair and efficient taxation system. This is the first data mining application specifically aimed at finding corporate residence fraud, where we show the predictive value of using both structured and fine-grained invoicing data. We further describe the problems involved in building such a fraud detection system, which are mainly data-related (e.g. data asymmetry, quality, volume, variety and velocity) and deployment-related (e.g. the need for explanations of the predictions made).
Language
English
Source (book)
KDD 2014 : data science for social good : proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, August 24-27, 2014, New York City
Publication
New York, N.Y. : ACM, 2014
ISBN
978-1-4503-2956-9
Volume/pages
p. 1650-1659
Full text (Publishers DOI)
Full text (publishers version - intranet only)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Record
Identification
Creation 19.10.2015
Last edited 22.11.2016
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