Preventing rather than punishing: An early warning model of malfeasance in public procurement

Jorge Gallego, Gonzalo Rivero, Juan Martínez

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

26 Citas (Scopus)


Is it possible to predict malfeasance in public procurement? With the proliferation of e-procurement systems in the public sector, anti-corruption agencies and watchdog organizations have access to valuable sources of information with which to identify transactions that are likely to become troublesome and why. In this article, we discuss the promises and challenges of using machine learning models to predict inefficiency and corruption in public procurement. We illustrate this approach with a dataset with more than two million public procurement contracts in Colombia. We trained machine learning models to predict which of them will result in corruption investigations, a breach of contract, or implementation inefficiencies. We then discuss how our models can help practitioners better understand the drivers of corruption and inefficiency in public procurement. Our approach will be useful to governments interested in exploiting large administrative datasets to improve the provision of public goods, and it highlights some of the tradeoffs and challenges that they might face throughout this process.

Idioma originalInglés estadounidense
PublicaciónInternational Journal of Forecasting
EstadoEn prensa - 2020

Áreas temáticas de ASJC Scopus

  • Gestión internacional y de empresa


Profundice en los temas de investigación de 'Preventing rather than punishing: An early warning model of malfeasance in public procurement'. En conjunto forman una huella única.

Citar esto