From Fishing to Catching: Developing Actionable Red Flags in Public Procurement to Prevent and Control Corruption
Peer Reviewed
Date issued
December 2022
Subject
Machine Learning;
Public Procurement;
Transparency and Anticorruption;
Procurement;
Corporate Corruption;
Procurement Management;
Government Revenue;
Digital Technology;
Public Investment;
Science and Technology;
Public Sector;
Big Data;
Regulation;
Public Employment;
Innovation;
Public Administration;
Fiscal Transparency;
Public Service
JEL code
D73 - Bureaucracy • Administrative Processes in Public Organizations • Corruption;
H57 - Procurement;
K14 - Criminal Law;
K23 - Regulated Industries and Administrative Law;
K24 - Cyber Law;
K42 - Illegal Behavior and the Enforcement of Law;
O31 - Innovation and Invention: Processes and Incentives;
H50 - National Government Expenditures and Related Policies: General;
H83 - Public Administration • Public Sector Accounting and Audits;
L78 - Government Policy;
O54 - Latin America • Caribbean
Country
Paraguay
Category
Technical Notes
How can entities responsible for public procurement more reliably detect collusion and other irregular behavior? Most of the existing red flag tools are based on ex post analysis of public procurement data and are not integrated into national procurement systems. This does not allow them to identify irregularities in a timely manner, negatively affecting the efficiency and transparency of public spending. This document describes the red flags solution implemented in Paraguay, which contributes to solving these problems. It combines rule-based and machine learning algorithms to provide public officials with accurate, real-time information to reliably detect irregularities in the procurement process, without reducing efficiency.
