The document used to be the proof
For as long as referencing has existed, a payslip meant something. A bank statement, a reference letter, a passport scan: these were treated as reliable proxies for the facts they represented. If the numbers matched and the format looked right, the underlying reality was assumed to match too. Letting agents built entire referencing workflows on that assumption, and for a long time it held.
It no longer holds, because the cost of producing a convincing fake has collapsed. Generating a payslip that carries the right typography, the right employer logo, the right National Insurance format and a plausible pay history now takes minutes and requires no forgery skill at all. The same is true of reference letters, tenancy histories and identity documents. The tenant referencing sector is not facing more forgery. It is facing a different kind of evidence problem, where the fake is not a poor copy of the real thing but a synthetic original with no real thing behind it at all.
The scale is no longer anecdotal
This is not a theoretical risk. Goodlord’s analysis of over a million tenant references found that suspected fraud rose by almost 40% during 2025, with fake employment references up 226.6% year-on-year and referee fraud and identity manipulation both surging as fraudsters target the verification process itself rather than just the paperwork, as reported by Letting Agent Today.
Where the checks were built for a different problem
Tenant referencing typically runs at a single point in the process: application submission. A prospective tenant uploads payslips or bank statements to evidence income, a reference from a current or previous landlord, sometimes a letter from an employer, and identity documents to satisfy Right to Rent checks. A referencing analyst or letting agent reviews this bundle, cross-checking figures against declared income, checking that names and addresses are internally consistent, and occasionally placing a call to a referee.
This review process was designed to catch human error and clumsy forgery: a payslip with the wrong tax year, a reference letter with an inconsistent job title, numbers that don’t add up. It was never designed to catch a document that is internally consistent because it was generated to be internally consistent. AI-generated payslips don’t have typos in the tax code because the model wasn’t copying a real payslip badly, it was generating a plausible one from scratch. The same applies to reference letters and, increasingly, to entire fabricated identities built from a combination of a bogus employer, a doctored ID and an invented referee who will confirm whatever the applicant needs confirmed.
This is the shift that matters for anyone running a referencing team: the fraud has moved from the document to the identity behind it. A single forged payslip is one point of failure to check. A coherent fake identity, with a matching employer, a plausible referee and consistent paperwork across every touchpoint, is a system built to pass every check an agent has time to run. Reviewers are good at spotting inconsistency and implausibility. They are not equipped to spot pixel-level generation artefacts, font irregularities that don’t register consciously, or the reuse of a document template across multiple unrelated applications, because none of that was ever part of the job description.
What this costs the referencing desk
The operational pressure this creates is straightforward but unforgiving. Referencing volumes have not gone down, and every application still needs a decision within a commercially sensible timeframe. Agents cannot escalate every application for manual deep-checking without breaking turnaround times and frustrating genuine tenants, most of whom are exactly who they say they are. But treating every application as low risk means the fabricated ones, the ones built specifically to look ordinary rather than suspicious, pass straight through.
That is the real danger here: synthetic evidence is not designed to look perfect, it is designed to look unremarkable. A convincing fake payslip doesn’t draw attention to itself. It sits in the pile with forty other legitimate ones and gets the same glance. The false-negative risk isn’t a rare edge case, it’s the default outcome of a system with finite review time and an applicant population that increasingly includes people using the same accessible tools. And the data suggests fraud is not evenly distributed either, with certain regions and higher-value properties carrying meaningfully higher exposure, which means uniform scrutiny is both inefficient and misdirected.
Applying scrutiny where it’s actually needed
This is where an authenticity layer changes the shape of the problem rather than the workload. Humanly analyses the documents and images submitted as part of a referencing application, whether that’s a payslip, a bank statement, a reference letter or an identity document, and surfaces explainable signals that may indicate AI generation or manipulation, directly inside the review workflow the agent already uses.
The principle is selective confidence: scrutiny applied proportionally, not uniformly. Instead of every application getting the same cursory check or every application triggering a manual escalation, the referencing team gets a signal that tells them where a closer look is warranted and, just as importantly, where it isn’t. This is decision support, not decision automation. Humanly does not approve or reject a tenancy application; it gives the human reviewer the same kind of contextual judgement they already bring to a case, plus visibility into the kind of artefact they were never trained to see. Human first, AI guarded.
For a sector where document authenticity used to be assumed and is now the actual point of failure, that additional layer of evidence integrity is not a nice-to-have. It is what lets a referencing team keep moving at volume without quietly waving fabricated identities through.
Talk to us
If your referencing workflow needs a way to spot AI-generated or manipulated documents without slowing down every genuine application, speak to our sales team about adding an authenticity layer to your existing process.
