Brazilian Government Procurements: an Approach to Find Fraud Traces in Companies Relationships

• Rebeca A. Baldomir CGU
• Gustavo C. G. Van Erven CGU
• Célia Ghedini Ralha UNB

DOI: https://doi.org/10.5753/eniac.2018.4464

Resumo

Data mining has been an area of high visibility in recent years and many researches have shown good efficiency in this area to find information in large databases. This paper presents an approach to find fraud traces applying data mining techniques to public databases of the Brazilian Federal Government bidings. The aim is to find evidence of fraud, such as stunts and cartels. The task of finding fraud evidences in large amount of data is complex for auditors since they have correlate data. The proposed approach was used to develop a prototype which has been used by auditors in the Ministry of Transparency and General Comptroller of the Union (CGU).

Referências

[1] L. A. Joia and F. Zamot, “Internet-based reverse auctions by the brazilian government,” The Electronic Journal of Information Systems in Developing Countries, vol. 9, no. 1, pp. 1–12, 2002.

[2] S. Rose-Ackerman and B. J. Palifka, Corruption and government: Causes, consequences, and reform. Cambridge university press, 2016.

[3] G. C. van Erven, M. Holanda, and R. N. Carvalho, “Detecting evidence of fraud in the brazilian government using graph databases,” in World Conference on Information Systems and Technologies. Springer, 2017, pp. 464–473.

[4] C. G. Ralha and C. V. S. Silva, “A multi-agent data mining system for cartel detection in brazilian government procurement,” Expert Systems with Applications, vol. 39, no. 14, pp. 11 642–11 656, 2012.

[5] J. Han and M. Kamber, Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, Inc, 2005.

[6] R. Agrawal and R. Srikant, “Fast algorithms for mining association rules in large databases,” in Proceedings of the 20th International Conference on Very Large Data Bases, ser. VLDB ’94. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 1994, pp. 487–499.

[7] Brasil, “Lei no 8.666, de 21 de junho de 1993,” Jun. 1993, disponível em: http : ==www:planalto:gov:br=ccivil03=leis=L8666cons:html. Acessado em: 07/11/2017.

[8] T. Nguyen, Z. Li, V. Spiegler, P. Ieromonachou, and Y. Lin, “Big data analytics in supply chain management: A state-of-the-art literature review,” Computers & Operations Research, 2017.

[9] N. Hein and F. Kreuzber, “Aplicação da análise fatorial como ferramenta de data mining no desempenho social das empresas do setor de consumo cíclico listadas na bmfbovespa,” 2014.

[10] R. de Padua, E. L. S. Junior, L. P. do Carmo, V. O. de Carvalho, and S. O. Rezende, “Preprocessing data sets for association rules using community detection and clustering: a comparative study.”

[11] M. Miroslav, M. Miloš, Š. Velimir, D. Božo, and L. Ðord¯e, “Semantic technologies on the mission: Preventing corruption in public procurement,” Computers in industry, vol. 65, no. 5, pp. 878–890, 2014.

[12] G. Van Erven, W. Silva, R. Carvalho, and M. Holanda, “Graphed: A graph description diagram for graph databases,” in World Conference on Information Systems and Technologies. Springer, 2018, pp. 1141–1151.