Bid fraud alerts: A social media-based approach
Abstract
In Brazil, public bids must guarantee transparency and free competition between bidders. However, monitoring irregularities is complex because it involves a huge volume of data and a small number of specialists. In this context, this work proposes the use of a methodology based on the concepts of audit trails and social networks to raise fraud alerts, to assist in the fight against corruption. The characterization and analysis of a real social network, associated with a case study of a possible fraudulent bid, reveal that the methodology presented is able to identify suspicious bids, identified by a set of audit trails.References
Abidi, W. U. H. et al. (2021). Real-time shill bidding fraud detection empowered with fussed machine learning. IEEE Access, 9:113612–113621.
Andrade, P. H. M. A. et al. (2020). Auditing government purchases with a multicriteria anomaly detection strategy. J. Inf. Data Manag., 11(1).
Anowar, F. and Sadaoui, S. (2019). Multi-class ensemble learning of imbalanced bidding fraud data. In Canadian AI, volume 11489 of Lecture Notes in Computer Science, pages 352–358. Springer.
Araújo, J. L. et al. (2021). Caracterização de caminhos mais prováveis em uma rede complexa de processos jurídicos. In BraSNAM, pages 44–54, Porto Alegre, Brasil. SBC.
Barabási, A.-L. (2016). Network science. Cambridge University Press.
Elshaar, S. and Sadaoui, S. (2020). Detecting bidding fraud using a few labeled data. In ICAART, pages 17–25. SCITEPRESS.
Ganguly, S. and Sadaoui, S. (2018). Online detection of shill bidding fraud based on machine learning techniques. In IEA/AIE, volume 10868 of Lecture Notes in Computer Science, pages 303–314. Springer.
Grace, E. et al. (2016). Detecting fraud, corruption, and collusion in international development contracts: The design of a proof-of-concept automated system. In IEEE BigData, pages 1444–1453. IEEE Computer Society.
Lima, M. et al. (2020). Inferring about fraudulent collusion risk on Brazilian public works contracts in official texts using a Bi-LSTM approach. In EMNLP, pages 1580–1588, Online. ACL.
Pereira, R. and Murai, F. (2021). Quão efetivas são redes neurais baseadas em grafos na detecção de fraude para dados em rede? In BraSNAM, pages 205–210, Porto Alegre, Brasil. SBC.
Ralha, C. G. and Silva, C. V. S. (2012). A multi-agent data mining system for cartel detection in brazilian government procurement. Exp. Syst. Appl., 39(14):11642–11656.
Velasco, R. B. et al. (2021). A decision support system for fraud detection in public procurement. Int. Trans. Oper. Res., 28(1):27–47.
Vlasselaer, V. V. et al. (2015). AFRAID: fraud detection via active inference in timeevolving social networks. In ASONAM, pages 659–666. ACM.
Andrade, P. H. M. A. et al. (2020). Auditing government purchases with a multicriteria anomaly detection strategy. J. Inf. Data Manag., 11(1).
Anowar, F. and Sadaoui, S. (2019). Multi-class ensemble learning of imbalanced bidding fraud data. In Canadian AI, volume 11489 of Lecture Notes in Computer Science, pages 352–358. Springer.
Araújo, J. L. et al. (2021). Caracterização de caminhos mais prováveis em uma rede complexa de processos jurídicos. In BraSNAM, pages 44–54, Porto Alegre, Brasil. SBC.
Barabási, A.-L. (2016). Network science. Cambridge University Press.
Elshaar, S. and Sadaoui, S. (2020). Detecting bidding fraud using a few labeled data. In ICAART, pages 17–25. SCITEPRESS.
Ganguly, S. and Sadaoui, S. (2018). Online detection of shill bidding fraud based on machine learning techniques. In IEA/AIE, volume 10868 of Lecture Notes in Computer Science, pages 303–314. Springer.
Grace, E. et al. (2016). Detecting fraud, corruption, and collusion in international development contracts: The design of a proof-of-concept automated system. In IEEE BigData, pages 1444–1453. IEEE Computer Society.
Lima, M. et al. (2020). Inferring about fraudulent collusion risk on Brazilian public works contracts in official texts using a Bi-LSTM approach. In EMNLP, pages 1580–1588, Online. ACL.
Pereira, R. and Murai, F. (2021). Quão efetivas são redes neurais baseadas em grafos na detecção de fraude para dados em rede? In BraSNAM, pages 205–210, Porto Alegre, Brasil. SBC.
Ralha, C. G. and Silva, C. V. S. (2012). A multi-agent data mining system for cartel detection in brazilian government procurement. Exp. Syst. Appl., 39(14):11642–11656.
Velasco, R. B. et al. (2021). A decision support system for fraud detection in public procurement. Int. Trans. Oper. Res., 28(1):27–47.
Vlasselaer, V. V. et al. (2015). AFRAID: fraud detection via active inference in timeevolving social networks. In ASONAM, pages 659–666. ACM.
Published
2022-07-31
How to Cite
COSTA, Lucas L. et al.
Bid fraud alerts: A social media-based approach.
Proceedings of the Brazilian Workshop on Social Network Analysis and Mining (BraSNAM), [S.l.], p. 37-48, july 2022.
ISSN 2595-6094.
Available at: <https://sol.sbc.org.br/index.php/brasnam/article/view/20515>. Date accessed: 18 may 2024.
doi: https://doi.org/10.5753/brasnam.2022.223175.
Section
Full Papers
