This platform aims to raise the awareness of project implementation units (PIUs) that are implementing World Bank-financed projects about the most common warning signs of fraud and corruption to facilitate their understanding of the mechanics and “red flags” of fraud and corruption.
PIUs consist of borrower government staff and technical consultants. The borrower government has the responsibility for World Bank-financed project preparation and implementation and most borrowers typically contract with consultants and private sector firms for goods, workers, and services, as needed, during the project's different phases.
This platform is primarily intended to guide project implementation units (PIUs) on how to use the red flag warnings as tools to identify for what they should look in terms of corruption and fraud risks during the different stages of World Bank-financed project execution. In this context, the red flags are seen as a first-line indicator of possible fraud and corruption, and the triggering of any given flag can lead to follow-up or due diligence. Red flag lists can be extensive, but the flags listed here are some of the most common warning signs PIU should look out for during project preparation and implementation. The red flags are often linked to procurement or implementation outcomes that can have multiple causes.
There are multiple warning signs that can help identify possible risks of fraud and corruption in World Bank-financed projects. These Warning Signs aim to guide PIUs to spot possible fraud and corruption risks during the different stages of World Bank-financed project preparation and implementation.
Project managers and key officials of PIUs can protect projects from fraud and corruption by watching out for common red flags. This interactive module demonstrates how PIUs can use the red flag warnings to spot possible corruption and fraud risks at various project preparation and implementation stages, such as the typical stages World Bank projects go through. The module involves both information- and case-based learning and is meant to demonstrate how such flags may be detected.