When you market credit cards and other financial products under fair-lending rules, deleting a protected field like age or race isn't enough. The model rebuilds it from related clues. Intelafy leaves protected data out before it scores anyone. Your compliance officer gets an exact record of what produced every score.
A model built without an age or race field can still rebuild both from related clues (ZIP code, name, neighborhood). Fair-lending audits catch it only after the fact, once the model is already live. The problem is the approach itself, not how you patch it.
When the engine leaves protected data out, it's gone: the scoring process can't see it and can't use it. An auditor gets proof, not a promise.
When someone asks why a specific person scored highly, you don't reach for an after-the-fact estimate. The engine recorded the exact, named signals that drove that person's score at the moment it scored them.
Print the features behind any score. Prove the protected classes were never in the pool. Close the fair-lending finding with evidence, not narrative.
Regulation B rules built into how scores are made, not bolted on at review time.
Between scheduled recalibrations, a score changes only when the data changes, so you can tell whether the campaign worked or the audience changed.
One scale for every product line (card, auto, mortgage, personal loan), all measured the same way.
Intelafy's methods are designed to be compliant with fair-lending rules (FCRA / Regulation B) and can be tailored to your definitions. Whether any specific use is compliant depends on your data, your use, and your counsel.
Hand your compliance team an audit trail. Pilot on your acquisition file this quarter.
