Developed by a team of specialists from the Instituto Politécnico Nacional (IPN), this application of computational models aims to enhance transparency in public procurement by processing large volumes of data and identifying potential risk areas.
Adda Avendaño
By applying computational models to analyze patterns in public procurement, a specialized team from the Instituto Politécnico Nacional (IPN) developed a program capable of processing large datasets, identifying significant trends, and highlighting potential risk areas.
With this program, the IPN team, named Data Donkey, won first place in the sixth edition of the 2024 Anti-Corruption Datathon, an international competition organized by the Executive Secretariat of the National Anti-Corruption System (SESNA). The event aimed to strengthen anti-corruption systems through open-source technology and open data from the platforms that integrate the National Digital Platform (PDN) and the National Anti-Corruption System (SNA).
The competition featured 29 teams from Bolivia, Colombia, the United States, Mexico, Nicaragua, Peru, and the Dominican Republic. They were all tasked with developing technological tools capable of detecting potential irregularities in Mexico’s public procurement processes.
“The high volume of public procurement procedures and the increasing use of direct awards make effective oversight and risk pattern detection challenging,” noted the IPN specialists.
The team comprises Vidal Salazar Sánchez, faculty member at the Interdisciplinary Professional Unit in Engineering and Advanced Technologies (UPIITA); Jorge Luis León Acevedo, Head of the Academic Management Department at the Directorate of Upper Secondary Education (DEMS); and Gabriel Campos Cervantes, Analyst at the Directorate of Budgeting and Programming, all from IPN divisions.
The project, explained the winners, consists of an application of computational models for the analysis of patterns in public procurement data, a tool that seeks to make them more transparent, since it allows processing large volumes of data and detecting possible areas of risk.
They added that their proposal was developed with open-source data and software, as indicated in the call for proposals, and focuses on four fundamental areas of analysis for public transparency: data centering, direct awards, temporal distribution of awards, and identification of monopolistic suppliers.
They reported that the system is scalable and offers key badges to highlight dynamics of economic concentration, identify recurrent practices of direct awards, visualize seasonal patterns in public contracting, and detect possible monopolistic dependencies in certain categories; it is also capable of handling millions of records and can be integrated into government auditing systems to support real-time decision making.
The tool designed by the polytechnicians will promote transparency in public contracting, in addition to establishing a replicable framework for comparative studies and more equitable public policies, for which reason they plan to integrate machine learning algorithms for predictive analysis in the future, which will allow for more accurate detection of irregularities.
INSTITUTO POLITÉCNICO NACIONAL
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