Most customers who fail a know your customer check are real people who simply do not look the way the check expects. They have moved house recently, share a name with a relative, or have barely touched credit. When verification leans on credit reference agency data alone, those people return too few matches, get rejected, and walk away from an onboarding flow they had every right to complete.
The cost lands on the business first. Every wrongly failed customer is acquisition spend already paid and revenue that never arrives. For regulated onboarders running high volumes, a quiet false-failure rate of even a few per cent turns into a measurable drag on growth, and it tends to hit the youngest and newest customers hardest.
Failing a KYC check usually means an electronic verification could not match enough of the customer’s identity details against enough independent, reliable sources to satisfy the firm’s standard. It rarely means the person is fraudulent. More often the data held about them is thin, out of date, or split across records that do not line up cleanly.
The common cause is a data gap. The applicant is genuine; the records held about them are too thin to confirm it. Electronic checks confirm identity by matching attributes such as name, address and date of birth across reliable sources. When those sources hold little or nothing about a person, the check returns too few matches to pass, even though the customer is genuine and willing.
Four patterns account for most false failures.
|
Why the check fails |
What is happening |
|
Thin credit file |
The person has little borrowing history, so credit-reference sources hold few records to match against. |
|
No CRA footprint |
Recent arrivals, young adults and the financially excluded may not appear in credit data at all. |
|
Attribute mismatch |
A recent move, a maiden name, an abbreviated first name or a typo means the data does not line up across sources. |
|
Stale records |
The customer is real, but the underlying source has not caught up with their current address or status. |
A thin file means the credit reference agencies hold very little about a person, because they have rarely borrowed, never held a card, or only recently entered the system. A KYC check built mainly on credit data has almost nothing to match against, so it returns a low score and rejects a customer who is perfectly legitimate.
This is not a small group. FCA data puts the scale of financial exclusion in the millions, and that population overlaps heavily with the people who fail credit-based identity checks.
Around 7.1 million UK adults, roughly one in seven, are financially excluded (FCA Financial Lives 2024). Not everyone excluded fails KYC, but the overlap is large. People outside mainstream financial services are precisely the ones credit-reference-only checks struggle to verify.
The point for an onboarding team is proportion. When roughly one in seven adults sits in or near this group, a check that depends on credit data is structurally likely to reject a meaningful slice of genuine applicants. The newer and younger your customer base, the larger that slice tends to be.
Consider what happens at the customer’s end. Someone opens an account, types in their name, address and date of birth, and is told they cannot be verified. They have done nothing wrong, and no obvious way to fix it. Most will not call support to argue the point. They close the tab and open one with a competitor instead.
Even customers with a footprint fail when their attributes do not match cleanly across sources. A house move that one source has recorded and another has not, a married name against a maiden name, “Mike” on one record and “Michael” on another, all reduce the match count. The customer is real and the details are honest, but the check cannot reconcile them.
Stale data compounds this. Sources update on different cycles, so a person who moved six months ago may match their old address in one place and their new address in another. The check sees inconsistency where there is none, and a genuine customer is failed on a timing problem they cannot see.
The fix is to widen the sources the check runs against while holding the verification standard exactly where it is. When a customer fails a credit-reference-only check, a second pass against additional independent sources often finds the matches the first check missed. The customer still has to be verified to the same level. They are simply checked against data that actually holds a record of them.
This second pass is sometimes called a data wash or second wash. In plain terms, it is a second look at the records that failed first time, run against a wider set of independent sources, banks, mobile networks, insurance data, public sector data and finance applications alongside credit reference agencies. People invisible to credit data frequently show up clearly in these.
Recovery rates can be substantial in practice. A Tier-1 gaming operator was failing credit-reference-only checks on close to 30 per cent of new customers. A second pass over those failures matched data on 55 per cent of the sample. Around half of the previously rejected customers were onboarded, an overall 15 per cent uplift in new-customer onboarding.
Widening the sources does not change what the firm owes. The match step confirms identity. Risk assessment, source of funds where it applies, and ongoing monitoring remain the firm’s responsibility under its wider customer due diligence. A stronger match simply means fewer good customers are lost before that work can even begin.
KYC Match, part of OneID, performs that wider match step. It checks name, address and date of birth across banks, mobile networks, insurance data, public sector data, finance applications and credit reference agencies, in whatever combination you configure, and returns the count of independent sources that confirmed each record. OneID is a digital verification services provider certified under the UK’s Digital Verification Services Trust Framework.
Most failures come from a data gap, not fraud. Electronic checks match identity attributes against reliable sources. When a customer has a thin credit file, no credit footprint, or details that do not line up across records, the check returns too few matches and rejects someone who is genuine.
No. A thin file only limits what credit-reference sources can confirm. The same person often matches clearly against banks, mobile networks or public sector data. A check that draws on those wider sources can verify thin-file customers that credit data alone misses.
The FCA Financial Lives 2024 survey puts financial exclusion at around 7.1 million UK adults, roughly one in seven. That population overlaps heavily with the people who fail credit-based identity checks, which is why credit-reference-only verification rejects a meaningful share of genuine applicants.
Often, yes. Running the failed records through a second pass against additional independent sources frequently finds the matches the first check missed, to the same verification standard. The customer is recovered by checking against data that actually holds a record of them, at the same verification standard as the first check.
You can find out exactly how many good customers your current checks reject. Run up to 1,000 of your records through KYC Match for free and compare the match counts against your existing provider, on your own data, before you change anything. Contact OneID to set up your comparison.