Two questions lenders keep conflating
Lending asks two different questions, and most stacks answer them with one blunt instrument.
Can we accept this customer? — a question about risk: their track record, their conduct, the negative events on file. And: what can they sustainably repay, and on what terms? — a question about liquidity: the actual money moving through the account each month once everything else is paid.
These are not the same question. They don’t move together, and they are not best answered by the same data. Risk is historical and relative. Liquidity is present-tense and absolute. Collapse them into one score and you accept people you shouldn’t, decline people you should be serving, and set repayment terms that break the moment life wobbles.
Credit Canary decisions them separately: risk for acceptance, liquidity for repayment and terms. The rest of this paper is the evidence for why, and the architecture that delivers it.
The bureau lottery
If you decision risk off one bureau — and 91% of applications use exactly one1 — you’ve taken a single, noisy read of a moving target.
The FCA’s Credit Information Market Study put this beyond argument. It matched the same ~48,000 people across all three large CRAs, ranked each person within each bureau, and asked a simple question: does the same customer land in the same place? Mostly, they don’t. Only 16–29% sit in the same risk decile across two bureaus, and 16–36% are three or more deciles apart.1
This is not a reason to abandon the bureau. Negative events, conduct history and relative ranking are exactly what a CRA is for, and we use it for precisely that: acceptance. It’s a reason never to mistake one bureau’s read for the whole truth — and never to read affordability off it.
Income is the field that lies — and forms make it worse
If risk is noisy across bureaus, income is worse. The same study found more than 54% of people had an income that differed by two or more deciles depending on the agency1 — and income is the input your affordability assessment leans on hardest.
Now layer on the form. The industry likes to say customers don’t fill in forms honestly. That’s the wrong diagnosis. Customers aren’t lying — they don’t know. An application form assumes a level of recall and financial self-knowledge almost nobody has: monthly outgoings to the pound, every subscription, the real cost of childcare this month, exactly what leaves the account on payday. People answer with a hopeful estimate, because that’s the best the format allows.
A form doesn’t catch liars. It catches everyone — because it asks people to recall a financial life they’ve never had to hold in their head.
So we stop asking the question the form can’t answer. We verify instead. Open banking transactions show real income hitting the account. Direct debit data shows the committed outgoings a customer would never list from memory — the credit agreements, utilities, BNPL, the subscriptions. Then we do the thing forms never do: we play it back. “We can see around £X going to these commitments each month — does that look right, and is anything missing?”
Validation beats interrogation. The customer corrects and confirms a picture we’ve already built, instead of constructing it cold from memory. That’s the split in one line: CRA data answers risk; open banking and direct debit reality answer affordability, repayment amount and terms.
One missing field can decide the whole case
There’s a second reason the data has to be complete, not just accurate: a single fact can flip the entire decision. Lenders told the FCA they would automatically decline on one data point alone.
When one variable can flip the outcome, a partial picture isn’t a smaller version of the right answer. It’s a different answer. And the bureau you happen to pull is, as Figure 1 showed, partly a matter of chance.
One corroborated view of the customer
No single source is trusted on its own. We overlap them — and confidence lives in the overlap.
Multiple bureaus, open banking transactions, direct debit payments, income-statement uploads and the customer’s own validation all feed the same picture. Where two or more independent sources agree, we trust it. Where they diverge, that’s the signal — and the agents go to work. The output is one clean view of the customer that no single bureau, no form and no lone open-banking feed produces by itself.
From that corroborated base we resolve the four things that actually decide whether a loan is affordable:
Validated income — not what was declared, but income we can see arriving and the customer confirms. Credit exposure — the real repayment commitments, from bureau lines and the direct debits servicing them. Essential spending — utilities, rent, council tax: the non-negotiables. And discretionary spending — what’s left, read from transactions and balanced against ONS household-spend benchmarks, so we can judge whether the picture is plausible and how much genuine headroom exists.
banking
debits
uploads
benchmark
validation
That single view is what the rest of the engine runs on — and the next question is simply how complete it is.
The Data Quality Score
This is why every Credit Canary decision begins with a question about the data itself, before any question about the customer: how complete and corroborated is the picture we’re standing on?
The Data Quality Score runs across the variables that determine whether a decision is safe to make — among them identity and bureau coverage (thin-file and no-hit flags), bureau depth, whether declared income is corroborated by open banking, transaction-history depth, whether direct-debit commitments reconcile with what the customer told us, and the confidence of the resulting affordability read.
A high score means we have enough to decide well. A low score doesn’t trigger a decline — it triggers more questioning. Our AI agents take the gaps the score exposes and act on them: ask the customer a targeted follow-up, request an additional connection, reconcile a discrepancy, and play the findings back to confirm. The score drives the conversation; the agents run it; the customer ends up validating a complete picture rather than seeding a thin one.
Explainable by construction
Two engines sit on top of that validated picture.
The risk model decides acceptance, grounded in explainable machine learning — glass-box by design, every decision carrying the reason codes behind it. In a Consumer Duty and SM&CR world, a risk model you can’t explain isn’t a model you can deploy: customers are owed a reason, regulators an audit trail, and your own credit committee the logic. Accuracy you can’t account for is a liability, not an edge.
The liquidity model decides repayment amount and terms — built on the verified affordability surface, not the self-reported one. It’s what lets us say not just yes, but yes, this much, on these terms, and here’s the headroom if income dips.
Risk for acceptance. Liquidity for repayment. Both explainable. Neither guessed.
What this means
The data the whole industry relies on is noisier than it admits. Bureau risk reads diverge, income data diverges further, and the form was never going to fix it — because the problem was never honesty, it was recall.
The answer isn’t a better form or a bigger model on worse inputs. It’s to ask the right question of the right data, verify instead of ask, fill the gaps deliberately with a data-quality score and the agents to act on it, play the findings back to the customer, and keep the whole chain explainable end to end.
Two questions. Two data sources. One picture you can stand behind — and explain.