VigIA: prioritizing public procurement oversight with machine learning models and risk indices
Public procurement is a fundamental aspect of public administration. Its vast size makes its oversight and control very challenging, especially in countries where resources for these activities are limited. To support decisions and operations at public procurement oversight agencies, we developed an...
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| Language: | English |
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Cambridge University Press
2024-01-01
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| Series: | Data & Policy |
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| Online Access: | https://www.cambridge.org/core/product/identifier/S263232492400083X/type/journal_article |
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| author | Andrés Salazar Juan F. Pérez Jorge Gallego |
| author_facet | Andrés Salazar Juan F. Pérez Jorge Gallego |
| author_sort | Andrés Salazar |
| collection | DOAJ |
| description | Public procurement is a fundamental aspect of public administration. Its vast size makes its oversight and control very challenging, especially in countries where resources for these activities are limited. To support decisions and operations at public procurement oversight agencies, we developed and delivered VigIA, a data-based tool with two main components: (i) machine learning models to detect inefficiencies measured as cost overruns and delivery delays, and (ii) risk indices to detect irregularities in the procurement process. These two components cover complementary aspects of the procurement process, considering both active and passive waste, and help the oversight agencies to prioritize investigations and allocate resources. We show how the models developed shed light on specific features of the contracts to be considered and how their values signal red flags. We also highlight how these values change when the analysis focuses on specific contract types or on information available for early detection. Moreover, the models and indices developed only make use of open data and target variables generated by the procurement processes themselves, making them ideal to support continuous decisions at overseeing agencies. |
| format | Article |
| id | doaj-art-3789ff48f2b44eea9566d22de611cbf8 |
| institution | OA Journals |
| issn | 2632-3249 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Data & Policy |
| spelling | doaj-art-3789ff48f2b44eea9566d22de611cbf82025-08-20T02:32:15ZengCambridge University PressData & Policy2632-32492024-01-01610.1017/dap.2024.83VigIA: prioritizing public procurement oversight with machine learning models and risk indicesAndrés Salazar0https://orcid.org/0009-0002-1182-2926Juan F. Pérez1https://orcid.org/0000-0003-4732-1621Jorge Gallego2https://orcid.org/0000-0002-9398-9553Department of Economics, Universidad del Rosario, Bogotá, Colombia.Department of Industrial Engineering, Universidad de los Andes, Bogotá, ColombiaOffice of Evaluation and Oversight, Inter-American Development Bank, Washington, DC, USAPublic procurement is a fundamental aspect of public administration. Its vast size makes its oversight and control very challenging, especially in countries where resources for these activities are limited. To support decisions and operations at public procurement oversight agencies, we developed and delivered VigIA, a data-based tool with two main components: (i) machine learning models to detect inefficiencies measured as cost overruns and delivery delays, and (ii) risk indices to detect irregularities in the procurement process. These two components cover complementary aspects of the procurement process, considering both active and passive waste, and help the oversight agencies to prioritize investigations and allocate resources. We show how the models developed shed light on specific features of the contracts to be considered and how their values signal red flags. We also highlight how these values change when the analysis focuses on specific contract types or on information available for early detection. Moreover, the models and indices developed only make use of open data and target variables generated by the procurement processes themselves, making them ideal to support continuous decisions at overseeing agencies.https://www.cambridge.org/core/product/identifier/S263232492400083X/type/journal_articleGovTech solutionsmachine LearningPublic procurement |
| spellingShingle | Andrés Salazar Juan F. Pérez Jorge Gallego VigIA: prioritizing public procurement oversight with machine learning models and risk indices Data & Policy GovTech solutions machine Learning Public procurement |
| title | VigIA: prioritizing public procurement oversight with machine learning models and risk indices |
| title_full | VigIA: prioritizing public procurement oversight with machine learning models and risk indices |
| title_fullStr | VigIA: prioritizing public procurement oversight with machine learning models and risk indices |
| title_full_unstemmed | VigIA: prioritizing public procurement oversight with machine learning models and risk indices |
| title_short | VigIA: prioritizing public procurement oversight with machine learning models and risk indices |
| title_sort | vigia prioritizing public procurement oversight with machine learning models and risk indices |
| topic | GovTech solutions machine Learning Public procurement |
| url | https://www.cambridge.org/core/product/identifier/S263232492400083X/type/journal_article |
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