PLUS: A Semi-automated Pipeline for Fraud Detection in Public Bids

Author:

Brandão Michele A.1,Reis Arthur P. G.2,Mendes Bárbara M. A.2,de Almeida Clara A. Bacha2,Oliveira Gabriel P.2,Hott Henrique2,Gomide Larissa D.2,Costa Lucas L.2,Silva Mariana O.2,Lacerda Anisio2,Pappa Gisele L.2

Affiliation:

1. Instituto Federal de Minas Gerais, Brazil Universidade Federal de Minas Gerais, Brazil

2. Universidade Federal de Minas Gerais, Brazil

Abstract

The diversity of sources and formats of public bidding documents makes collecting, processing, and organizing such documents challenging from the point of view of data analysis. Thus, the development of approaches to deal with such data is relevant since the analysis of them allows to expand of the inclusion of people as they have more access to public decisions and expenditures, increase transparency in the public sector and give citizens a greater sense of responsibility for having different points of view on the government’s performance in meeting its public policy goals. In this context, we propose PLUS, a semi-automated pipeline for fraud detection in public bids. PLUS comprises a heuristic meta-classifier for bidding documents and a data quality module. Both modules present promising results after a proof of concept, reinforcing the relevance of PLUS for automating the bidding process investigation. Then, we present two applications of PLUS on real-world data: the construction of audit trails for fraud detection and a price database for overpricing detection. Such applications evidence a significant reduction of specialists’ work searching for irregularities in public bids.

Publisher

Association for Computing Machinery (ACM)

Subject

Public Administration,Software,Information Systems,Computer Science Applications,Computer Networks and Communications

Reference30 articles.

1. Miguel Arana-Catania , Felix-Anselm van Lier , Rob Procter , Nataliya Tkachenko , Yulan He , Arkaitz Zubiaga , and Maria Liakata . 2021. Citizen Participation and Machine Learning for a Better Democracy. Digit. Gov. Res. Pract. 2, 3 ( 2021 ), 27:1–27:22. https://doi.org/10.1145/3452118 10.1145/3452118 Miguel Arana-Catania, Felix-Anselm van Lier, Rob Procter, Nataliya Tkachenko, Yulan He, Arkaitz Zubiaga, and Maria Liakata. 2021. Citizen Participation and Machine Learning for a Better Democracy. Digit. Gov. Res. Pract. 2, 3 (2021), 27:1–27:22. https://doi.org/10.1145/3452118

2. An Overview of Data Quality Frameworks

3. Emilio Feliciano de Oliveira and Milene Selbach Silveira . 2018 . Open government data in Brazil a systematic review of its uses and issues . In Proceedings of the 19th Annual International Conference on Digital Government Research: Governance in the Data Age, DG.O 2018 , Delft, The Netherlands, May 30 - June 01, 2018, Marijn Janssen, Soon Ae Chun, and Vishanth Weerakkody (Eds.). ACM, 60:1–60:9. https://doi.org/10.1145/3209281.3209339 10.1145/3209281.3209339 Emilio Feliciano de Oliveira and Milene Selbach Silveira. 2018. Open government data in Brazil a systematic review of its uses and issues. In Proceedings of the 19th Annual International Conference on Digital Government Research: Governance in the Data Age, DG.O 2018, Delft, The Netherlands, May 30 - June 01, 2018, Marijn Janssen, Soon Ae Chun, and Vishanth Weerakkody (Eds.). ACM, 60:1–60:9. https://doi.org/10.1145/3209281.3209339

4. Kellyton dos Santos Brito , Marcos Antônio da Silva Costa , Vinicius Cardoso Garcia , and Silvio Romero de Lemos Meira . 2014 . Experiences Integrating Heterogeneous Government Open Data Sources to Deliver Services and Promote Transparency in Brazil . In IEEE 38th Annual Computer Software and Applications Conference, COMPSAC 2014 , Vasteras, Sweden , July 21-25, 2014. IEEE Computer Society, 606–607. https://doi.org/10.1109/COMPSAC.2014.87 10.1109/COMPSAC.2014.87 Kellyton dos Santos Brito, Marcos Antônio da Silva Costa, Vinicius Cardoso Garcia, and Silvio Romero de Lemos Meira. 2014. Experiences Integrating Heterogeneous Government Open Data Sources to Deliver Services and Promote Transparency in Brazil. In IEEE 38th Annual Computer Software and Applications Conference, COMPSAC 2014, Vasteras, Sweden, July 21-25, 2014. IEEE Computer Society, 606–607. https://doi.org/10.1109/COMPSAC.2014.87

5. Harald Foidl , Michael Felderer , and Rudolf Ramler . 2022 . Data Smells: Categories, Causes and Consequences, and Detection of Suspicious Data in AI-based Systems. CoRR abs/2203.10384. https://doi.org/10.48550/arXiv.2203.10384 10.48550/arXiv.2203.10384 Harald Foidl, Michael Felderer, and Rudolf Ramler. 2022. Data Smells: Categories, Causes and Consequences, and Detection of Suspicious Data in AI-based Systems. CoRR abs/2203.10384. https://doi.org/10.48550/arXiv.2203.10384

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