Risk-based predictive modelling for audit verification: evidence from EU-funded programmes

This study proposes a machine learning framework to support risk‑based verification of expenditure declarations in European Structural and Investment Funds, reflecting the current regulatory emphasis on proportional and data‑driven audit strategies. Quantitatively, the problem is formulated as an imbalanced three-class classification task with ordered outcomes on high-dimensional administrative data; the ordinal structure is exploited ex post in evaluation and error interpretation. The framework