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Data and Text Mining for the Detection of Fraud in Public Contracts: A Case Study of Ecuador’s Official Public Procurement System

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Part of the Lecture Notes in Electrical Engineering book series (LNEE,volume 846)

Abstract

Corruption is present in different forms and typologies, directly affecting the execution of both public and private contracts. The doctoral thesis aims to establish a methodology to prevent and detect corruption automatically in public procurement. By using machine learning techniques and Natural Language Processing (NLP), algorithms for detecting and predicting favouritism and oligopoly are developed. In addition to detecting corruption and its types in the Ecuadorian Public Procurement System (SERCOP) and also visualising the results in an appropriate way, in order to detect and prevent future acts of corruption. In order to analyse the feasibility of the study, a mapping and systematic literature review was carried out, allowing the hypothesis and the methodology to be followed in order to execute and evaluate the developed algorithms. Finally, the detection of favouritism based on process qualification parameters and types of contracting is tested.

Keywords

  • Corruption
  • Public procurement
  • Data mining
  • Machine learning

Doctorado en Ingeniería Informática Universidad de Salamanca.

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Notes

  1. 1.

    https://www.compraspublicas.gob.ec/ProcesoContratacion/compras/.

References

  1. Náez Gómez, J.E.: Relación entre el Índice de Control de la Corrupción y algunas variables sociales, económicas e institucionales. Nómadas. Rev. Crítica Ciencias Soc. y Jurídicas 38 (2013)

    Google Scholar 

  2. Brito-Gaona, L.F., Iglesias, E.M.: Inversión privada, gasto público, presión tributaria en América Latina. Estududios de Economia. 44, 5–30 (2017)

    CrossRef  Google Scholar 

  3. Izquierdo, A., Pessino, C., Vuletin, G.: Mejor gasto para mejores vidas: Cómo América Latina y el Caribe puede hacer más con menos, vol. 10. Inter-American Development Bank (2018)

    Google Scholar 

  4. Servicio Nacional de contratacion publica: Rendicion de cuentas (2018)

    Google Scholar 

  5. Moran, J.: Democratic transitions and forms of corruption. Crime, Law Soc. Chang. 36, 379–393 (2001).https://doi.org/10.1023/A:1012072301648

  6. Castro Cuenca, C.G.: La corrupción pública y privada: causas, efectos y mecanismos para combatirla - Google Play (2017)

    Google Scholar 

  7. Cassagne, J.C., Rivero Ysern, E.: La contratación pública, Hammurabi (2007)

    Google Scholar 

  8. Vargas-Hernández, J.G.: The Multiple Faces of Corruption: Typology, Forms and Levels. SSRN Electron. J. (2009). https://doi.org/10.2139/ssrn.1413976

  9. Ponce, H.G., Gil, M.T.N., Durán, M.P.: Responsible public procurement. Des. meas. indicators. CIRIEC-Espana Rev. Econ. Publica, Soc. y Coop. 44, 253–280 (2019)

    Google Scholar 

  10. Subdirección General de Control Coordinación Técnica de Controversias: Manual De Buenas Prácticas En La Contratación Pública Para El Desarrollo Del Ecuador. 1-46 (2015)

    Google Scholar 

  11. Alvarez-Jareño, J.A., Badal-Valero, E., Pavia, J.M.: Aplicación de métodos estadísticos, económicos y de aprendizaje automático para la detección de la corrupción. (2019)

    Google Scholar 

  12. Woodie, A.: Inside the Panama Papers: How Cloud Analytics Made It All Possible. https://www.datanami.com/2016/04/07/inside-panama-papers-cloud-analytics-made-possible/. Accessed 12 Aug 2019

  13. Controladoria-Geral da União: Observatório da Despesa Pública - Controladoria-Geral da União. http://www.cgu.gov.br/assuntos/informacoes-estrategicas/observatorio-da-despesa-publica. Accessed 12 Aug 2019

  14. Torres-Carrión, P.V., Gonzalez-Gonzalez, C.S., Aciar,S., Rodriguez-Morales, G.: Methodology for systematic literature review applied to engineering and education. In: IEEE Global Engineering Education Conference, EDUCON 2018-April, pp. 1364–73 (2018)

    Google Scholar 

  15. Torres-Berru, Y., López-Batista, V.F., Torres-Carrión, P.: Data mining to detect and prevent corruption in contracts: Systematic mapping review. RISTI - Rev. Iber. Sist. e Tecnol. Inf. 2020, 13–26 (2020)

    Google Scholar 

  16. Torres Berru, Y., López Batista, V.F., Torres-Carrión, P., Jimenez, M.G. : Artificial Intelligence techniques to detect and prevent corruption in procurement: a systematic literature review. In: Botto-Tobar M., et al. (eds) Applied Technologies. ICAT 2019. Communications in Computer and Information Science, vol. 1194. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-42520

  17. Hotelling, H.: A generalized T test and measure of multivariate dispersion. In: Proceedings of the Second Berkeley Symposium on Mathematical Statistics and Probability, pp. 23–41 (1951)

    Google Scholar 

  18. Ultsch, A., Mörchen, F.: ESOM-Maps: tools for clustering, visualization, and classification with Emergent SOM, pp. 1–7. Tech. Rep. Dept. Math. Comput. Sci. Univ. Marburg Ger (2005)

    Google Scholar 

  19. Saurkar, A.V., Gode, S.A.: An overview on web scraping techniques and tools. Int. J. Futur. Revolut. Comput. Sci. Commun. Eng. 4, 363–367 (2018)

    Google Scholar 

  20. Merkl, D.: Text classification with self-organizing maps: some lessons learned. Neurocomputing 211–3, 61–77 (1998)

    CrossRef  Google Scholar 

  21. Vettigli, G.: MiniSom: minimalistic and NumPy-based implementation of the Self Organizing Map

    Google Scholar 

  22. Kohonen, T.: Self-organizing Maps. Springer-Verlag, Berlin (1995)

    CrossRef  Google Scholar 

  23. Kotsiantis, S.B.: Supervised machine learning: a review of classification techniques. Informatika (2007)

    Google Scholar 

  24. Ortiz-Prado, E., Fernandez-Naranjo, R., Torres-Berru, Y., Lowe, R., Torres, I.: Exceptional prices of medical and other supplies during the COVID-19 pandemic in Ecuador. Am. J. Trop. Med. Hyg. 105, 81–87 (2021). https://doi.org/10.4269/ajtmh.21-0221

    CrossRef  Google Scholar 

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Correspondence to Yeferson Torres-Berru .

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Torres-Berru, Y., López Batista, V.F. (2022). Data and Text Mining for the Detection of Fraud in Public Contracts: A Case Study of Ecuador’s Official Public Procurement System. In: Berrezueta, S., Abad, K. (eds) Doctoral Symposium on Information and Communication Technologies - DSICT. Lecture Notes in Electrical Engineering, vol 846. Springer, Cham. https://doi.org/10.1007/978-3-030-93718-8_10

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  • DOI: https://doi.org/10.1007/978-3-030-93718-8_10

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