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Sofia, Chief Risk Officer at EuroBank, watches loan applications fly through her dashboard. The AI model that calculates creditworthiness gives a green or red signal in less than a second. Until recently, that speed was enough to stay ahead of the competition. But since the EU AI Act, the opposite applies: no explanation = no consent. When a young entrepreneur posts his rejection on LinkedIn ("They won't tell me why!"), Sofia realizes that speed without transparency can become a PR disaster.
Credit scoring is explicitly listed as high-risk in Annex III of the AI Act1. This means:
Non-compliance is not a theoretical risk: authorities can request model logs, impose fines, and shut down systems.
EuroBank uses credit scoring not only for mortgages, but also for credit cards, working capital loans, and dynamic interest rates. That model therefore directly influences access prices to financial products. A model that structurally underscores freelancers or penalizes certain postal codes immediately leads to discriminatory outcomes and reputational damage.
The human-in-the-loop principle means more than an employee clicking approve. Sofia trains her front-office team to understand model variables: why does device type contribute? How heavily does payment history weigh versus cash flow? When in doubt, a file is put on-chain for manual reassessment, with justification.
Where customers previously only saw "rejected," EuroBank now shows:
| Route | Action | Result |
|---|---|---|
| 1. Variable mapping | Document origin, measurement scale, and potential bias risk of each feature | Complete overview of model inputs and their justification |
| 2. Fairness testing | Compare acceptance rates between age groups, sectors, and regions | Quantitative bias detection and mitigation strategies |
| 3. Explain layers | Show the three most important score drivers in customer portals | Transparent communication in understandable language |
| 4. Override logging | Log every manual change for periodic re-training | Feedback loop for continuous model improvement |
| 5. AI literacy | Make credit advisors co-owners of model performance | Competent teams that can assess and explain models |
Document the origin, measurement scale, and potential bias risk of each feature the model uses.
Compare acceptance rates between age groups, sectors, and regions to detect structural bias.
Show the three most important score drivers in customer portals in understandable language.
Every manual change feeds periodic re-training and model recalibration.
Make credit advisors co-owners of model performance; organize quarterly sessions with data scientists2.
Within two months, the number of complaints about "unexplainable" rejections drops by 30%. Customers appreciate the transparent explanation and accept rejections faster. At the same time, the team discovers that a handful of features are outdated; removing them increases model precision and reduces indirect discrimination.
Through insight into the driver variables, pricing becomes sharper: less cross-subsidy between low and high-risk customers. The marketing department uses the insights to better target products, while risk teams free up time for real analysis instead of incident management. Transparency proves to be a commercial advantage.
After credit scoring, we'll dive into:
Each blog builds on the same core: AI compliance as a strategic advantage, not as a cost center.
Curious about how to make your credit scoring model AI Act-proof? Embed AI develops modular training and audit trajectories – from data due diligence to explainability dashboards. Feel free to get in touch to exchange ideas.
Discover in 5 minutes whether your AI systems comply with the new EU AI Act legislation. Our interactive tool gives you immediate insight into compliance risks and concrete action steps.