Abstract proposals are easy to misread. These pages work through concrete situations where the Databanking model changes the outcome — what information moves, what doesn't, and why it matters.
A retailer needs to know whether a customer is over 18. Step by step through the full query lifecycle — deposit, algorithm submission, sandbox execution, single-bit result, audit trail — with pseudocode and the bit-budget arithmetic.
Every hospital, insurer, pharmacy, and research programme currently keeps its own copy of the same medical record. Databanking replaces duplication with controlled access — and lets research consent become a deliberate, per-study choice rather than a blanket waiver.
The DGA's Chapter III on data intermediation services is the closest real-world precedent. A clause-by-clause comparison of where the two align, where the DGA falls short, and where new legislation would still be needed.
The UK Online Safety Act requires platforms to check users' ages, and Ofcom is actively enforcing it. Every platform currently runs its own check from scratch. Databanking lets the verification happen once; every subsequent platform gets a yes/no predicate, not the underlying document or face scan.
A taxi operator needs to route a driver to a pickup and drop-off. It does not need a copy of your journey history. A booking token materialises into addresses on the driver's dashboard, is deleted after the ride, and a link token answers audit and discount questions with a yes or a no.
To erase your data from a data broker, you currently have to hand that broker the very information you want them to delete. Databanking resolves the paradox: a lossy fingerprint, a sandbox match, and a single yes/no answer — no raw data ever crosses the boundary.