Overpricing Analysis in Brazilian Public Bidding Items

Authors

DOI:

https://doi.org/10.5753/jis.2024.3831

Keywords:

Overpricing, Public Bidding, Fraud, Methodology, Statistical, Purchase

Abstract

Analyzing overpricing in public bidding items is essential for government agencies to detect signs of fraud in acquiring public goods and services. In this context, this paper presents two main contributions: a methodology for processing and standardizing bid item descriptions and a statistical approach for overpricing detection using the interquartile range. We evaluated a comparative analysis of three distinct grouping strategies, each emphasizing different facets of the item description standardization process. Furthermore, to gauge the efficacy of both proposed approaches, we leveraged a ground-truth dataset for a thorough evaluation containing quantitative and qualitative analyses. Overall, our findings suggest that the evaluated strategies are promising for identifying potential irregularities within public bidding processes.

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References

Brandão, M. A. et al. (2023). Plus: A semi-automated pipeline for fraud detection in public bids. Digit. Gov.: Res. Pract. Just Accepted. DOI: 10.1145/3616396.

Brandão, M. A. et al. (2023). Impacto do pré-processamento e representação textual na classificação de documentos de licitações. In Proceedings of the 38th Brazilian Symposium on Databases, SBBD 2023, Belo Horizonte, MG, Brazil, September 25-29, 2023, pages 102–114. SBC. DOI: 10.5753/sbbd.2023.231658.

Brasil (2018). Law no. 13.709 of august 14, 2018: Brazilian general data protection law (lgpd). [link] Accessed: 16 January 2024.

Coelho, G. M. C. et al. (2022). Text classification in the brazilian legal domain. In Filipe, J., Smialek, M., Brodsky, A., and Hammoudi, S., editors, Proceedings of the 24th International Conference on Enterprise Information Systems, ICEIS 2022, Online Streaming, April 25-27, 2022, Volume 1, pages 355–363. SCITEPRESS. DOI: 10.5220/0011062000003179.

Constantino, K. et al. (2022). Segmentação e classificação semântica de trechos de diários oficiais usando aprendizado ativo. In SBBD, pages 304–316, Porto Alegre, RS, Brasil. SBC. DOI: 10.5753/sbbd.2022.224656.

Correa, M. A. O. S. and Leal, A. G. (2018). Identification of overpricing in the purchase of medication by the federal government of brazil, using text mining and clustering based on ontology. In Proceedings of the 2018 2nd International Conference on Cloud and Big Data Computing, ICCBDC 2018, Barcelona, Spain, August 03-05, 2018, pages 66–70. ACM. DOI: 10.1145/3264560.3264569.

Costa, L. L. et al. (2022). Alertas de fraude em licitações: Uma abordagem baseada em redes sociais. In Proceedings of the 11th Brazilian Workshop on Social Network Analysis and Mining, pages 37–48, Porto Alegre, RS, Brasil. SBC. DOI: 10.5753/brasnam.2022.223175.

Gabardo, A. C. and Lopes, H. S. (2014). Using social network analysis to unveil cartels in public bids. In 2014 European Network Intelligence Conference, pages 17–21. DOI: 10.1109/ENIC.2014.11.

Green, R., Zimmerer, T., and Steadman, M. (1994). The role of buyer sophistication in competitive bidding. Journal of Business & Industrial Marketing, 9:51–59. DOI: 10.1108/08858629410053489.

Hott, H. R. et al. (2023). Evaluating contextualized embeddings for topic modeling in public bidding domain. In Naldi, M. C. and Bianchi, R. A. C., editors, Intelligent Systems - 12th Brazilian Conference, BRACIS 2023, Belo Horizonte, Brazil, September 25-29, 2023, Proceedings, Part III, volume 14197 of Lecture Notes in Computer Science, pages 410–426. Springer. DOI: 10.1007/978-3-031-45392-2_27.

Lima, M. C. et al. (2020). Inferring about fraudulent collusion risk on brazilian public works contracts in official texts using a bi-lstm approach. In Cohn, T., He, Y., and Liu, Y., editors, Findings of the Association for Computational Linguistics: EMNLP 2020, Online Event, 16-20 November 2020, volume EMNLP 2020 of Findings of ACL, pages 1580–1588. Association for Computational Linguistics. DOI: 10.18653/V1/2020.FINDINGS-EMNLP.143.

Luna, R. and Figueiredo, D. (2022). Caracterização das licitações públicas no estado do rio de janeiro: Diversidade, licitantes Únicos e redes. In WCGE, pages 145–156, Porto Alegre, RS, Brasil. SBC. DOI: 10.5753/wcge.2022.222675.

Matschak, T., Trang, S., and Prinz, C. (2022). A taxonomy of machine learning-based fraud detection systems. In Beck, R., Petcu, D., Fotache, M., Matook, S., Helms, R., Wiener, M., Rusu, L., and Tuunanen, T., editors, 30th European Conference on Information Systems - New Horizons in Digitally United Societies, ECIS 2022, Timisoara, Romania, June 18-24, 2022.

Monteiro, M. d. S., Batista, G. O. d. S., and Salgado, L. C. d. C. (2023). Investigating usability pitfalls in brazilian and foreign governmental chatbots. Journal on Interactive Systems, 14(1):331–340. DOI: 10.5753/jis.2023.3104.

Oliveira, G. P. et al. (2022). Detecting inconsistencies in public bids: An automated and data-based approach. In WebMedia, pages 193–201, Porto Alegre, RS, Brasil. SBC. DOI: 10.1145/3539637.3558230.

Oliveira, G. P. et al. (2023). Assessing data quality inconsistencies in brazilian governmental data. Journal of Information and Data Management, 14(1). DOI: 10.5753/jidm.2023.3220.

Pereira, A. et al. (2022). Usando redes complexas na identificação de empresas fraudulentas em licitações públicas. In WCGE, pages 13–24, Porto Alegre, RS, Brasil. SBC. DOI: 10.5753/wcge.2022.222704.

Pereira, G. et al. (2021). Classificação taxonômica de categorias de serviços públicos para aplicações digitais. In WCGE, pages 119–130, Porto Alegre, RS, Brasil. SBC. DOI: 10.5753/wcge.2021.15982.

Reis, V. Q. d., Rabello, M. E. R., Lima, A. C., Jardim, G. P. S., Fernandes, E. R., and Brefeld, U. (2023). Data practices in apps from brazil: What do privacy policies inform us about? Journal on Interactive Systems, 14(1):1–8. DOI: 10.5753/jis.2023.2954.

Silva, M. et al. (2023). Análise de sobrepreço em itens de licitações públicas. In Anais do XI Workshop de Computação Aplicada em Governo Eletrônico, pages 118–129, Porto Alegre, RS, Brasil. SBC. DOI: 10.5753/wcge.2023.230608.

Silva, M. O. et al. (2022). Lipset: Um conjunto de dados com documentos rotulados de licitações públicas. In SBBD DSW, pages 13–24, Porto Alegre, RS, Brasil. SBC. DOI: 10.5753/dsw.2022.224925.

Xiao, Z. and Jiao, J. (2021). Explainable fraud detection for few labeled time series data. Secur. Commun. Networks, 2021:9941464:1–9941464:9. DOI: 10.1155/2021/9941464.

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Published

2024-01-18

How to Cite

SILVA, M. O.; COSTA, L. G. L.; GOMIDE, L. D.; BEZERRA, G.; OLIVEIRA, G. P.; BRANDÃO, M. A.; LACERDA, A.; PAPPA, G. Overpricing Analysis in Brazilian Public Bidding Items. Journal on Interactive Systems, Porto Alegre, RS, v. 15, n. 1, p. 130–142, 2024. DOI: 10.5753/jis.2024.3831. Disponível em: https://sol.sbc.org.br/journals/index.php/jis/article/view/3831. Acesso em: 29 apr. 2024.

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Section

Regular Paper

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