Detecting Inconsistencies in Public Bids: An Automated and Data-based Approach

  • Gabriel P. Oliveira UFMG
  • Arthur P. G. Reis UFMG
  • Felipe A. N. Freitas UFMG
  • Lucas L. Costa UFMG
  • Mariana O. Silva UFMG
  • Pedro P. V. Brum UFMG
  • Samuel E. L. Oliveira UFMG
  • Michele A. Brandão UFMG / IFMG
  • Anisio Lacerda UFMG
  • Gisele L. Pappa UFMG


One application for using government data is the detection of irregularities that may indicate fraud in the public sector. This paper presents an approach that analyzes public bidding data available on the Web to detect bidder inconsistencies. Specifically, we propose a hierarchical decision approach from public bidding data, where each bidder is classified as Valid, Doubtful, or Invalid, based on the compatibility between the bidding items and the divisions of the CNAE codes (National Classification of Economic activities). The results reveal that combining commonly available data on bidders and extracting the description of bid items can help in fraud detection. Furthermore, the proposed approach can reduce the number of bids a specialist must analyze to detect fraud, making it easier to identify inconsistencies.

Palavras-chave: Human-Computer Interaction, Assistive Technology, Accessibility, Autism spectrum disorders


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OLIVEIRA, Gabriel P. et al. Detecting Inconsistencies in Public Bids: An Automated and Data-based Approach. In: SIMPÓSIO BRASILEIRO DE SISTEMAS MULTIMÍDIA E WEB (WEBMEDIA), 28. , 2022, Curitiba. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2022 . p. 193-201.

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