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 possibly a major contributor in the slowing down of economies in general. Although corporate residence fraud is known to be a major factor in this trend, 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).
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