The EU AI Act makes risk classification central to AI governance. For financial institutions, the practical challenge is not simply labelling a model high or low risk; it is demonstrating that the classification matches the system's purpose, deployment context and effect on people.
Start with the function, not the algorithm
A gradient-boosted model, neural network and rules engine can all create material compliance risk if they influence access to credit, insurance, employment or other regulated outcomes. The inventory should describe the decision and its consequences, not just the model family.
Evidence expected for a high-risk system
Evidence should cover risk management, data governance, technical documentation, record keeping, transparency, human oversight, accuracy, robustness and cybersecurity. The evidence pack should be versioned and linked to the exact model, data snapshot, thresholds and release decision used in production.
Human oversight must be real
A human reviewer who can only approve a machine recommendation without time, authority or information is not meaningful oversight. Define when review is mandatory, what information the reviewer receives, how overrides are recorded and how override patterns feed back into monitoring.
Read the official EU AI Act text and the European Commission regulatory overview. The exact obligations depend on the system and role of the organisation.