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

  • Leon Sólon da SilvaEmail author
  • Rommel Novaes Carvalho
  • João Carlos Felix Souza
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9265)

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.

Keywords

Tax compliance risk Tax refund Data mining Predictive models 

References

  1. 1.
    Brazilian institute of statistics and geography tax administration and customs website - ferederal revenue of brazil. http://www.ibge.gov.br. Accessed: 09 December 2014
  2. 2.
    Brazilian tax administration and customs website - ferederal revenue of brazil. http://www.receita.fazenda.gov.br. Accessed: 09 December 2014
  3. 3.
    González, P.C., Velásquez, J.D.: Characterization and detection of taxpayers with false invoices using data mining techniques. Expert Syst. Appl. 40(5), 1427–1436 (2013)CrossRefGoogle Scholar
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    Martikainen, J., et al.: Data Mining in Tax Administration-Using Analytics to Enhance Tax Compliance. Department of Information and Service Economy, Aalto University, Espoo (2012)Google Scholar
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    Watkins, R.C., Reynolds, K.M., Demara, R., Georgiopoulos, M., Gonzalez, A., Eaglin, R.: Tracking dirty proceeds: exploring data mining technologies as tools to investigate money laundering. Police Pract. Res. 4(2), 163–178 (2003)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Leon Sólon da Silva
    • 1
    • 2
    Email author
  • Rommel Novaes Carvalho
    • 1
    • 3
  • João Carlos Felix Souza
    • 1
  1. 1.University of Brasilia - UnBBrasília DFBrasil
  2. 2.Secretariat of Federal Revenue of Brazil-RFB BrasiliaBrasil
  3. 3.Office of the Comptroller General - CGUBrasiliaBrasil

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