Governance
Own every AI decision — and the record behind it
For the executive, compliance lead, or Senior Manager accountable for the AI and automated decisions a UK regulated firm relies on. When a model or automated process drives an outcome, someone will eventually ask who owned it and how it was made. The answer is often unclear — responsibility is spread across vendors, models, and staff, and the record has drifted from reality. You get named ownership for each decision, a record that traces input to outcome, and oversight arrangements your own team can run after we have gone. Fixed fee and timetable agreed before we start, after a twenty-minute call.
Built by practitioners who have done this work inside banks and regulated firms.
Accountability Chain
StableBusiness Owner / Accountable Lead
Ultimate accountability
Governance Owner
Oversight & approval
Data / Model / Risk Owners
Operational control
Audit & Compliance Review
Independent challenge
Accountability holds when ownership is named, decisions are traceable, and escalation routes are tested.
Where it goes wrong
Where accountability for AI decisions breaks down
Governance rarely fails at sign-off. It fails later — when the record has drifted, no one owns the decision, and the gap only surfaces once it is questioned.
Documentation drift
AI policies are written once and never revisited. By the time an issue arises, the documented process no longer matches how the model or automated decision is actually being used.
Diffuse accountability
Responsibility spreads across vendors, models, and staff. When an automated decision is questioned, nobody can say clearly who owned it and who approved it.
Gaps discovered too late
Gaps in review, monitoring, and deployment oversight often surface only after a complaint, loss event, or regulatory challenge.
What you get
Tested against how decisions are really made, not against the policy
Three things, each tested against how decisions are actually made rather than against policy documents — the same standard we apply to fraud and AML rules.
For a significant decision made solely by automated processing, under the Data (Use and Access) Act 2025, UK GDPR Article 22C requires safeguards that let the person be told a decision was made, make representations about it, obtain human intervention, and contest it. Those four are what an escalation route has to deliver in practice — not a policy stating that one exists.
Accountability
A named owner for each AI or automated decision, a documented approval path, and an escalation route that works in practice when a regulator or management needs answers.
Traceability
A reviewable chain from input to AI output to business action, including overrides, approvals, and changes — so a decision can be explained after the fact.
Operational control
Release, monitoring, and review arrangements strong enough to support ongoing use rather than one-off compliance language, written so your team runs them.
See the method run
This one can be shown to you, running
Accountability, traceability, and operational control are the three headings above. This automation applies all three to one real obligation: it reads a firm’s own AML control claims, tests whether they are adequate for the risks the firm itself identified, and produces the report to the governing body naming the parts they cannot rely on.
MLR 2017 reg 18 · SYSC 6.3
MLRO Annual Report and Risk Assessment
A report the MLRO signs. Guidance is not statute, and a report that claims otherwise over-states its own authority.
The automation behind it is a financial-regulation check — what it demonstrates is the mechanism above, running on a real rulebook. It is shown by invitation and walked through with you, because the cases inside it are constructed for the demonstration. Illustrative cases are here.
Starting
Start with the decision you would least like to explain
A short conversation is usually enough to identify where control is weakest and what needs to change first. It needs no data and no system access. Every enquiry is treated in confidence.