2014 CorporateResidenceFraudDetectio

• (Junqué de Fortuny et al., 2014) ⇒ Enric Junqué de Fortuny, Marija Stankova, Julie Moeyersoms, Bart Minnaert, Foster Provost, and David Martens. (2014). “Corporate Residence Fraud Detection.” In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2014) Journal. ISBN:978-1-4503-2956-9 doi:10.1145/2623330.2623333

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222014%22+Corporate+Residence+Fraud+Detection
• http://dl.acm.org/citation.cfm?id=2623330.2623333&preflayout=flat#citedby

Quotes

Author Keywords

• Corporate residence fraud; fraud detection; miscellaneous; structured data; transactional data

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).

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 CorporateResidenceFraudDetectio	Foster Provost Enric Junqué de Fortuny Marija Stankova Julie Moeyersoms Bart Minnaert David Martens			Corporate Residence Fraud Detection				10.1145/2623330.2623333		2014