Which financial services AI systems are high-risk under the EU AI Act?

Annex III names creditworthiness evaluation and risk pricing for life and health insurance. Fraud detection, document processing and internal analytics generally are not, unless they determine access to an essential service. The test is whether the output decides something about a person, not whether the data is financial.

Can AI document processing meet our data residency requirements?

Yes, if residency is a design input rather than a later question. That means choosing where inference runs before selecting a vendor, since retrofitting residency onto a system built against a US-hosted API usually means rebuilding it.

How do we evidence explainability for a credit decision?

By designing for it rather than adding it afterwards. You need the model version, the inputs as they stood at decision time, the output, and the human review step, recorded so a specific decision can be reconstructed months later. Post-hoc explanation tools do not substitute for that record.

Where do most financial services AI projects stall?

At model risk governance and data access approval, not at model performance. Both are predictable and both take longer than the build, which is why identifying the approvers in week one changes the timeline more than any technical choice.

AI za financijske usluge

AI za financijske usluge - od Due Diligencea do usklađenosti

Izgradili smo produkcijske AI sustave za Private Equity Due Diligence, analizu ugovora i financijsku dokumentnu inteligenciju. Stvarno iskustvo sa stvarnim financijskim podacima.

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EU AI Act visokorizični rok: 2. kolovoza 2026.

AI sustavi u financijskim uslugama klasificirani su kao visokorizični. Potpuna usklađenost - uključujući upravljanje rizicima, dokumentaciju i ljudski nadzor - potrebna je do kolovoza 2026.

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What AI use cases work in financial services under the EU AI Act?

The financial services AI that pays off soonest is the work that is high volume, document-heavy and currently done by expensive people: contract and NDA review, KYC document extraction, claims triage, and reconciliation. None of these decide anything about a customer, so they sit outside the EU AI Act's high-risk tier and carry ordinary engineering obligations rather than conformity assessment.

The high-risk cases are narrower than most teams expect. Annex III names creditworthiness evaluation and risk pricing for life and health insurance. If your system scores a person for access to credit or insurance, plan for the full obligation set. If it reads documents faster, you are in a much lighter regime.

The binding constraint is rarely the model. It is data residency, model risk governance sign-off, and whether the output lands in a system someone already uses.

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