The BridgeGap Working Paper Series presents the first edition by BridgeGap members Andrea Longobucco and Joras Ferwerda of the University of Utrecht, part of the Work Package 6 on Artificial Intelligence on “Fighting Corruption with Artificial Intelligence: Searching for Suitable Public Procurement Data in the EU”. This report provides a groundbreaking comparative analysis of the suitability of public procurement data for AI-driven anti-corruption analysis in the European Union and its candidate or associated countries.
Algorithms that seek to flag clusters of corruption or anomalies in public procurement require contextual information that procurement notices do not provide, such as details of the (ultimate) beneficial ownership of companies, whether their directors have donated to the ruling party or have a political connection, and whether rival bidders sit on each others’ boards. Without such additional information, risk scores tend to collapse into simple indicators for corruption. Despite increasing digitalization of procurement processes, key challenges remain in terms of data completeness, standardization, and accessibility.
AI-generated image with NanoBanana to explore the idea of Europe using AI to fight corruption
This research confirms that all EU Member States now publish procurement data of sufficient quality for training machine-learning models to detect corruption. The authors consider the whole data landscape, connecting public procurement data with information about beneficial owners, political connections, media ownership, and complaints. Croatia, Estonia, and Latvia stand out among the EU-27 as front-runners, while the Netherlands, France, Austria, Germany, the Czech Republic, and Portugal also appear broadly AI-ready. This study provides a detailed look at the quality and availability of procurement data and related information (such as political connections and company ownership) in different countries. These findings help us understand where AI tools can be used effectively already, and where improvements in data access or structure are still needed.
The BridgeGap Working Paper Series provides a platform for the early dissemination of research findings, fostering discussion among colleagues both within and outside the project. Managed by Utrecht University, each paper undergoes a review process conducted by researchers from the BridgeGap consortium.
We welcome comments and suggestions on the series. Please direct all correspondence to the series editor, Joras Ferwerda, Associate Professor at the Utrecht University School of Economics.