  The costs and impacts of government corruption range from impairing a
country's economic growth to affecting its citizens' well-being and safety.
Public contracting between government dependencies and private sector
instances, referred to as public procurement, is a fertile land of opportunity
for corrupt practices, generating substantial monetary losses worldwide. Thus,
identifying and deterring corrupt activities between the government and the
private sector is paramount. However, due to several factors, corruption in
public procurement is challenging to identify and track, leading to corrupt
practices going unnoticed. This paper proposes a machine learning model based
on an ensemble of random forest classifiers, which we call hyper-forest, to
identify and predict corrupt contracts in M\'exico's public procurement data.
This method's results correctly detect most of the corrupt and non-corrupt
contracts evaluated in the dataset. Furthermore, we found that the most
critical predictors considered in the model are those related to the
relationship between buyers and suppliers rather than those related to features
of individual contracts. Also, the method proposed here is general enough to be
trained with data from other countries. Overall, our work presents a tool that
can help in the decision-making process to identify, predict and analyze
corruption in public procurement contracts.
