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Comparative Analysis of Classification Algorithms Applied to Circular Trading Prediction Scenarios

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Electronic Government and the Information Systems Perspective (EGOVIS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13429))

Abstract

Goods and services trading taxation is the main source of revenue for Brazilian states, therefore its evasion directly affects public services. A particular case of evasion concerns the issuance and use of cold invoices – ones referring to transactions registered at Tax Administration, but which did not actually take place. Following the proposal by Mathews et al. [16], this work reports the application of classic supervised learning algorithms to identify circular trading behaviors involving taxpayers from Brazilian State of Goias, through the analysis of their goods and services trading operations. Experiments showed similar results to the original ones, but pointing to k-Nearest-Neighbours (and not Logistic Regression) as the most accurate technique for this purpose – given Brazilian context’s characteristics.

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Authors and Affiliations

  1. Informatics Institute, Federal University of Goias, Goiania, GO, Brazil

    Douglas Silva, Sergio T. Carvalho & Nadia Silva

Authors
  1. Douglas Silva
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  2. Sergio T. Carvalho
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  3. Nadia Silva
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Corresponding author

Correspondence to Douglas Silva .

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Editors and Affiliations

  1. Corvinus University of Budapest, Budapest, Hungary

    Andrea Kő

  2. National Research Council, Florence, Italy

    Enrico Francesconi

  3. Johannes Kepler Univversität Linz, Linz, Oberösterreich, Austria

    Gabriele Kotsis

  4. Vienna University of Technology, Vienna, Austria

    A Min Tjoa

  5. Johannes Kepler University of Linz, Linz, Austria

    Ismail Khalil

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Silva, D., Carvalho, S.T., Silva, N. (2022). Comparative Analysis of Classification Algorithms Applied to Circular Trading Prediction Scenarios. In: Kő, A., Francesconi, E., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Electronic Government and the Information Systems Perspective. EGOVIS 2022. Lecture Notes in Computer Science, vol 13429. Springer, Cham. https://doi.org/10.1007/978-3-031-12673-4_7

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  • DOI: https://doi.org/10.1007/978-3-031-12673-4_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-12672-7

  • Online ISBN: 978-3-031-12673-4

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