A chief risk officer checks lending policy against capital requirements at two levels. The
macro view, where every customer's income and commitments are added up to show how much of the book
would fall into financial stress. And the micro view, one customer's income and spending. Sheaf sits
over the AI making these decisions, so a recommendation that contradicts what a customer can afford is stopped before
it reaches them.
Illustrative view for a specialist lender · representative lending book, sample data · stress bands and behaviours follow an open-banking affordability model
2,204
Customers in the book
3.5%
In the red (Stressed) at current policy
£1.10m
Capital at risk (expected credit loss)
9
Unsafe AI credit decisions stopped (last 30 days)
88/100
How well AI decisions hold together
Macro · stress-test the book · every customer's income & commitments, added up
Apply an increase to all expenditure: loans, mortgages, groceries+0%Move the slider to model a rate rise or a cost-of-living shock, and watch customers move toward the red as their spare money disappears. Click a band to see the customers in it. Click a customer to see the income and spending behind their score.
Customers in the red
77
3.5% of the book · Stressed band
Capital at risk
£1.10m
expected credit loss across the book
Policy headroom
Within appetite
capital requirement covered at this shock
The AI agents on this book · what they propose, and what Sheaf lets through
Waiting. Move the slider past the appetite, or open a customer's file and ask the agent
AI credit agent working the book
🤖
No action proposed yet
About to–
Sheaf gate
Action–
Outcome
waiting
The AI checks & what each one says
Capital
–
Customer outcomes
–
Lending policy
–
Why it is stopped: the claims contradict each other in a loop
How well it holds together
–
result: pending
Replay an action
Customers in the Stressed band
Customer
Money in each month
Left over
Affordability score
Bureau score
Warning signs
One customer · the income & spending behind the score
Sell the book · the resilience score and the evidence pack a buyer receives
Resilience score for the whole book
–
at the shock on the slider above
Cohort
Now
Under the shock
Change
The evidence pack
Everything a buyer, a regulator, or the risk committee needs to check the book for themselves. Built from what happened on this page.
The pack is fingerprinted in your browser with SHA-256. In a live deployment every pack is signed and timestamped by an independent timestamp authority.
Why a CRO runs the book on Sheaf
Macro and micro, one view
Policy you can stress-test in seconds.
The whole book added up against capital requirements, and one click into any customer's real income and expenditure. Set risk appetite from where the capital actually sits.
Measure before you lend
Affordability the score can't see.
Income replaced by loans, rising gambling, bank charges, less and less spare money. The behaviours that put a customer in the red are shown before an AI acts on a score that looks fine.
Use AI, and show it is safe
Speed with an evidence trail.
Contradictions are caught and stopped, and every decision carries its evidence. The lender can use AI across the book faster with the risk measured, and the same record answers the regulator.
Illustrative. The lending book, every customer listed, the scores and the counts of stopped decisions are sample data
to show the shape of the view. There is no real customer behind them. Stress bands (Secure → Stressed), affordability
scoring, and the behaviour flags follow an open-banking affordability model. Filled with a lender's own
open-banking and credit data, this becomes the live credit-risk dashboard. · sheaf.one · Portfolio IQ