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Predictive Models on Tax Refund Claims - Essays of Data Mining in Brazilian Tax Administration

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Electronic Government and the Information Systems Perspective (EGOVIS 2015)

Abstract

One of the main goals of every tax administration is safeguarding tax justice. For that matter, accurate taxpayers’ auditing selection plays an important role. Current scenario of economic recession, budget cuts and tax professionals’ hiring difficulty combined with growth of both population and number of enterprises presents the necessity of a more efficiently approach from tax administration in order to meet its objectives. The present work intends to show how data mining techniques usage helps better understand the profile of non compliant tax payers who claim for tax refunds. Moreover, we present results on the adoption of predictive models towards selection improvement of those who claims that are more likely to be rejected in Federal Revenue of Brazil (RFB). Preliminary results shows that this approach is an efficient way for selecting tax payers rather than not using it.

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References

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  2. Brazilian tax administration and customs website - ferederal revenue of brazil. http://www.receita.fazenda.gov.br. Accessed: 09 December 2014

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Correspondence to Leon Sólon da Silva .

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da Silva, L.S., Carvalho, R.N., Souza, J.C.F. (2015). Predictive Models on Tax Refund Claims - Essays of Data Mining in Brazilian Tax Administration. In: Kő, A., Francesconi, E. (eds) Electronic Government and the Information Systems Perspective. EGOVIS 2015. Lecture Notes in Computer Science, vol 9265. Springer, Cham. https://doi.org/10.1007/978-3-319-22389-6_16

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  • DOI: https://doi.org/10.1007/978-3-319-22389-6_16

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22388-9

  • Online ISBN: 978-3-319-22389-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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