Data Mining to Identify Anomalies in Public Procurement Rating Parameters

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 a...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Yeferson Torres-Berru, Vivian F. López Batista
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	corruption public procurement self-organizing map support vector machine machine learning data mining Electronics TK7800-8360
Acceso en línea:	https://doaj.org/article/1e60b03ce11c42679afab5ee3a1b8ff8
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:1e60b03ce11c42679afab5ee3a1b8ff8
record_format	dspace
spelling	oai:doaj.org-article:1e60b03ce11c42679afab5ee3a1b8ff82021-11-25T17:25:32ZData Mining to Identify Anomalies in Public Procurement Rating Parameters10.3390/electronics102228732079-9292https://doaj.org/article/1e60b03ce11c42679afab5ee3a1b8ff82021-11-01T00:00:00Zhttps://www.mdpi.com/2079-9292/10/22/2873https://doaj.org/toc/2079-9292The 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 <i>clustering</i> (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.Yeferson Torres-BerruVivian F. López BatistaMDPI AGarticlecorruptionpublic procurementself-organizing mapsupport vector machinemachine learningdata miningElectronicsTK7800-8360ENElectronics, Vol 10, Iss 2873, p 2873 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	corruption public procurement self-organizing map support vector machine machine learning data mining Electronics TK7800-8360
spellingShingle	corruption public procurement self-organizing map support vector machine machine learning data mining Electronics TK7800-8360 Yeferson Torres-Berru Vivian F. López Batista Data Mining to Identify Anomalies in Public Procurement Rating Parameters
description	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 <i>clustering</i> (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.
format	article
author	Yeferson Torres-Berru Vivian F. López Batista
author_facet	Yeferson Torres-Berru Vivian F. López Batista
author_sort	Yeferson Torres-Berru
title	Data Mining to Identify Anomalies in Public Procurement Rating Parameters
title_short	Data Mining to Identify Anomalies in Public Procurement Rating Parameters
title_full	Data Mining to Identify Anomalies in Public Procurement Rating Parameters
title_fullStr	Data Mining to Identify Anomalies in Public Procurement Rating Parameters
title_full_unstemmed	Data Mining to Identify Anomalies in Public Procurement Rating Parameters
title_sort	data mining to identify anomalies in public procurement rating parameters
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/1e60b03ce11c42679afab5ee3a1b8ff8
work_keys_str_mv	AT yefersontorresberru dataminingtoidentifyanomaliesinpublicprocurementratingparameters AT vivianflopezbatista dataminingtoidentifyanomaliesinpublicprocurementratingparameters
_version_	1718412335520940032

Data Mining to Identify Anomalies in Public Procurement Rating Parameters

Ejemplares similares