Title
Corporate residence fraud detection Corporate residence fraud detection
Author
Faculty/Department
Faculty of Applied Economics
Publication type
conferenceObject
Publication
New York, N.Y. :ACM, [*]
Subject
Economics
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
ISBN - Hoofdstuk
978-1-4503-2956-9
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
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).
E-info
https://repository.uantwerpen.be/docman/iruaauth/b0ff88/128239.pdf
Handle