Title: A neural network approach for predicting corruption in public procurement

Authors: Iván Pastor Sanz; Félix J. López Iturriaga; David Blanco-Alcántara

Addresses: University of Valladolid, Valladolid, Spain ' University of Valladolid, Valladolid, Spain; National Research University Higher School of Economics, Moscow, Russia ' University of Burgos, Burgos, Spain

Abstract: We apply topic modelling and neural network algorithms to a sample of more than 70,000 public procurement tenders from 33 European countries between 2016 and 2018. Rather than identifying a binary indicator of possible corruption, we establish a more precise red-flag indicator with four different levels. We initially identify some selection criteria that are more present in tenders with a low number of received offers, a common proxy of corruption. Our model then detects different corruption risk profiles depending on the selection criteria reported in the procurement announcement. Tenders awarded based mainly on price criteria present a higher risk of low competition. Consequently, non-price evaluation criteria (technical quality, environmental issues, etc.) are useful indicators for preventing corruption.

Keywords: corruption; neural networks; public procurement; self-organising maps; topic modelling.

DOI: 10.1504/EJIM.2024.135944

European Journal of International Management, 2024 Vol.22 No.2, pp.175 - 197

Received: 28 Sep 2021
Accepted: 09 Feb 2022
Published online: 10 Jan 2024 *

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