A Network‐Enhanced Supervised Learning Framework for Early Prediction of Corruption Risk Indicators in Public Procurement Data

A tender receiving only one offer (single bidding) is generally recognized as a sign of restricted competition and a potential corruption risk indicator. Existing corruption risk models rely predominantly on ex post information, limiting their usefulness for early stage prediction. This paper introduces a network‐enhanced supervised learning framework that predicts single bidding outcomes at the time of tender announcement, using only features available at the first call for tender. The framework integrates conventional procurement attributes with historical buyer behaviour metrics and with relational features derived from annual buyer–supplier interaction networks, including centrality measures, community structure and node embeddings. To ensure temporal validity, all features are constructed exclusively from data available prior to tender publication. The methodology is evaluated across three European countries, Bulgaria, Croatia and Serbia, using country‐specific models to account for institutional and market heterogeneity. Results show that the Random Forest model enriched with network‐based and historical features consistently outperforms baselines relying solely on transactional attributes, achieving macro F1‐scores between and . These results demonstrate that single bidding can be forecast reliably at the time of tender announcement and highlight the value of incorporating relational structures and buyer behaviour into procurement analytics. The proposed framework offers a scalable foundation for early warning systems that support proactive integrity monitoring and more transparent procurement governance.