Medina-Hernández, M., Kertész, J. & Fazekas, M. (2026) Learning from sanctioned government suppliers: a machine learning and network science approach to detecting fraud and corruption in Mexico. Scientific Reports
Detecting fraud and corruption in public procurement remains a major challenge for governments worldwide. Most research to-date builds on domain-knowledge-based corruption risk indicators of individual contract-level features and some also analyses contracting network patterns. A critical barrier for supervised machine learning is the absence of confirmed non-corrupt (negative) examples, which makes …