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Article

Data Mining to Identify Anomalies in Public Procurement Rating Parameters

by
Yeferson Torres-Berru
1,2,3,* and
Vivian F. López Batista
1
1
Department of Computer Science and Automatics, University of Salamanca, 37008 Salamanca, Spain
2
Departamento de Investigación, Instituto Tecnologico Superior Sudamericano, Loja 1101608, Ecuador
3
Escuela de Ingeniería en Tecnologías de la Información, Universidad Internacional del Ecuador, Loja 1101608, Ecuador
*
Author to whom correspondence should be addressed.
Electronics 2021, 10(22), 2873; https://doi.org/10.3390/electronics10222873
Submission received: 15 October 2021 / Revised: 18 November 2021 / Accepted: 19 November 2021 / Published: 22 November 2021
(This article belongs to the Special Issue Big Data Privacy-Preservation)

Abstract

The awarding of public procurement processes is one of the main causes of corruption in governments, due to the fact that in many cases, contracts are awarded to previously agreed suppliers (favouritism); for this selection, the qualification parameters of a process play a fundamental role, seeing as due to their manipulation, bidders with high prices win, causing prejudice to the state. This study identifies processes with anomalies and generates a model for detecting possible corruption in the assignment of process qualification parameters in public procurement. A multi-phase model was used (the identification of anomalies and generation of the detection model), which uses different algorithms, such as clustering (K-Means), Self-Organizing map (SOM), Support Vector Machine (SVM) and Principal Component Analysis (PCA). SOM was used to determine the level of influence of each rating parameter, K-Means to create groups by clustering, semi-supervised learning with SVM and PCA to generate a model to detect anomalies in the processes. By means of a case study, four groups of processes were obtained, highlighting the presence of the group “null economic offer” where the values for the economic offer do not exceed 1%, and a greater weight is given to other qualification parameters, which include direct contracting. The processes in this cluster are considered anomalous. Following this methodology, a semi-supervised learning model is built for the detection of anomalies, which obtains an accuracy of 95%, allowing the detection of procedures where the aim is to benefit a particular supplier by means of the qualification assignment parameters.
Keywords: corruption; public procurement; self-organizing map; support vector machine; machine learning; data mining corruption; public procurement; self-organizing map; support vector machine; machine learning; data mining

Share and Cite

MDPI and ACS Style

Torres-Berru, Y.; López Batista, V.F. Data Mining to Identify Anomalies in Public Procurement Rating Parameters. Electronics 2021, 10, 2873. https://doi.org/10.3390/electronics10222873

AMA Style

Torres-Berru Y, López Batista VF. Data Mining to Identify Anomalies in Public Procurement Rating Parameters. Electronics. 2021; 10(22):2873. https://doi.org/10.3390/electronics10222873

Chicago/Turabian Style

Torres-Berru, Yeferson, and Vivian F. López Batista. 2021. "Data Mining to Identify Anomalies in Public Procurement Rating Parameters" Electronics 10, no. 22: 2873. https://doi.org/10.3390/electronics10222873

APA Style

Torres-Berru, Y., & López Batista, V. F. (2021). Data Mining to Identify Anomalies in Public Procurement Rating Parameters. Electronics, 10(22), 2873. https://doi.org/10.3390/electronics10222873

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