You own coverage you cannot staff, a bench that is thinning, and an examiner who now asks what your AI position is.
Named Plainly
The Reality
I.
Coverage pressure is structural, not cyclical.The book grows; the team does not. Every year the sample covers a little less of what you are accountable for, and the defense of the sampling methodology gets a little longer.
II.
The bench you inherited is not being replaced.The reviewers who could grade a borderline file from across the room are retiring. What they knew was never written down, and the hiring market is not producing more of them.
III.
Examiners expect an AI governance posture now.Whether you deploy AI or refuse it, you will be asked to show your reasoning, your controls, and your metrics. “We are watching the space” is aging poorly as an answer.
The Reframe
The strongest sentence a review function can say is changing — from “we sampled twenty percent and here is why that was enough” to “we reviewed everything, and here is exactly which files a person examined, and why those.”
What Changes
Coverage, Reallocated
I.
Every file gets a full draft review, every cycle.The agent does the production work — spreads, ratios, narrative — on the whole book. Sampling stops being a coverage argument and becomes what it should have been all along: a control.
II.
Your reviewers examine what warrants them.Deterministic triggers — rating proximity, leverage, liquidity, CRE takeout, documentation, events, model integrity — route the borderline files to people. Human depth concentrates where reasonable reviewers could differ.
III.
A person still signs every file.Batch sign-off on clear files, individual sign-off on watch files, full human review above that — and a permanent random audit of 5–10% of the clear tier so the boundaries stay honest. Rules route; your people decide.
Your Policy Is the Rulebook
Argus does not arrive with a rating philosophy. Your credit policy, your rating definitions, and your risk appetite are encoded as its operating rules; every escalation threshold is your number. Your internal models stay yours. It is your best reviewer’s judgment, applied to every file — your policy, without drift.
Today, Not Someday
For Your Committee
I.
The Standard.The Creditboard Standard for AI-Assisted Loan Review Coverage is vendor-neutral and written to be carried into a governance process: the coverage argument, the four tiers, the trigger categories, the random-audit control, and the metrics — numbered and citable.
II.
The RACI exhibit.The training’s accountability exhibit allocates every task in the review lifecycle across your roles and the machine — with the one assignment that is locked by construction: the AI is never Accountable. Free, unwalled, and built to be adapted to your own function.
III.
The metrics that make it governable.Escalation recall against adjudicated cases (target ≥ 98%), random-audit agreement on the clear tier (target ≥ 95%), and a standing rule that every adjudicated miss becomes a new trigger. These are the numbers your model risk team will ask for — lead with them.
A Peer Conversation
Talk to a practitioner.
Creditboard Advisory is run by people who have led credit review functions and defended conclusions to examiners. Bring your coverage problem; the first conversation is between peers.
Small, off-the-record sessions with other heads of credit review on coverage, AI governance, and what examiners are actually asking. Applications are reviewed personally.