A machine learning model to identify corruption in M\'exico's public procurement contracts

TL;DR

This paper introduces a machine learning ensemble model called hyper-forest to identify corrupt public procurement contracts in Mexico, emphasizing relationships between buyers and suppliers as key predictors.

Contribution

The paper presents a novel ensemble machine learning model for detecting corruption in public procurement, highlighting the importance of relational features over contract-specific ones.

Findings

The model correctly detects most corrupt and non-corrupt contracts.

Relational features between buyers and suppliers are the most critical predictors.

The method is adaptable to other countries' data.

Abstract

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.