Every letting agent worth their salt knows that tenancy fraud is no joke. But preventing it is a completely different ball game.
That’s why suspected cases of the practice are costing the UK's private rented sector up to £4.1 billion every year.
These skyrocketing costs owe much to the rapid development of Artificial Intelligence (AI), which helps fraudsters bypass traditional tenant reference checks.
But what specific techniques are they using? And what types of tenancy fraud should you look out for?
In this blog, we reveal all and arm you with the prevention strategies you need to get the upper hand.
When we consider the fastest-growing types of tenancy fraud, we’re not talking about a percentage point here or there. We’re talking about techniques that are doubling and even tripling in frequency.
🥉- The third-fastest growing has increased by 140%
🥈- The second-fastest growing has increased by 146%
🥇- The fastest growing has increased by 227%
Let’s dive deeper into each of these, starting with…
Identity manipulation is the third fastest-growing form of tenancy fraud, surging by 140% in a single year.
In the past, this type of fraud was more straightforward: a fraudster would slap their photo onto someone else’s passport and hope no one would look too closely.
Today’s approach is far more calculated.
Scammers now craft synthetic identities, blending real details (like a genuine address or National Insurance number) with fabricated names and dates of birth to establish a clean credit profile.
That profile is then backed up with digitally altered documents, using AI image generators and advanced editing tools to produce doctored passports, tweaked machine-readable zone (MRZ) codes, and altered expiry dates so that every document tells the same consistent story.
This multi-document alignment is what makes synthetic identity fraud almost impossible to catch through traditional referencing and manual review alone.
Building a convincing synthetic persona requires time, planning, and capital, which is why fraudsters target properties where the payout justifies the effort.
Premium properties renting for over £10,000 a month have a confirmed fraud rate of 20 per 1,000 applications, roughly six times the mainstream market average.
This is where Identity Document Validation Technology (IDVT) comes into play. Using biometric AI, liveness analysis, and police database checks, it digitally confirms that the person applying genuinely matches their documents, rather than leaving that judgement to a manual review.
Just as importantly, no single check should be relied on in isolation, which is precisely the weakness synthetic identities are built to exploit.
Dive deeper into our report's findings by watching the dedicated webinar 👆
Referee fraud is the second-fastest-growing threat in tenant vetting, soaring by 146% year-on-year in 2025.
Where fake employment references involve someone vouching for an applicant's job, referee fraud tends to centre on rental history instead. The applicant provides contact details for a “former landlord” who then confirms a tenancy that never happened: the right address, a flawless payment history, and zero property damage.
It is rarely a solo effort. As Nishma Parekh, Goodlord's Director of Referencing, explains:
“Rather than one applicant acting alone, there may be several people involved, each supporting a different part of the application.”
When tenant referencing relies on manual phone calls or email questionnaires, fraudsters control both ends of the conversation. To a busy agent making a standard follow-up call, the response sounds completely legitimate and ticks all the usual boxes.
And it's about to get harder still.
Nicola Harding, Head of Referencing Operations at Goodlord, warns that fraudulent tenants could soon "provide someone else's contact details and then use AI voice technology to impersonate them."
This is precisely why referee fraud is accelerating so quickly. It's cheap to attempt, since all it needs is a phone number and a rehearsed answer. Plus, spreading fabrication across several accomplices makes the whole scheme harder to unpick or pin on any single individual.
Referee fraud is difficult to detect in the call itself, given that a well-rehearsed answer can sound exactly like a genuine one. The only reliable route is verifying the underlying claims independently.
Trusted Sources such as Open Banking, direct-to-payroll checks, and HMRC records confirm facts straight from the source. For instance, when a fake landlord claims a tenant paid £1,500 a month on time, source data instantly reveals whether those rent transactions actually left the applicant's bank account.
This data-first approach is why trusted sources have already enabled Goodlord to identify 83% of referee fraud on its platform.
Fake employment references are now the fastest-growing type of tenancy fraud, up by 227% during 2025.
In the past, this was relatively easy to catch. A quick web search or a dead phone line was usually enough to unravel a dodgy employer. That’s no longer the case.
As Mary Waterfield, Financial Crimes Manager at Goodlord, explains:
"Previously we saw a lot of fraudulent income documents, provided by individuals declaring fake companies... Recently, we have seen an increase in false employment references being provided by individuals within large and well-known companies."
In the last two years alone, Goodlord has had to alert both the NHS and a major telecommunications company directly after discovering that internal employees were issuing false references.
And that is precisely where fraud risk lies.
When the organisation is genuine, and the reference comes from an authentic email address within that organisation, traditional referencing processes can be circumvented.
Throw AI into the mix, and the risk multiplies. Fraudsters now routinely use generative AI tools to craft bespoke payslips that mirror a target company's exact formatting. When paired with a reference from a verified corporate email, the whole package suddenly looks completely above board.
The fix isn't reading references more carefully. It’s not to rely on the human reference as a source of truth in the first place. Direct-to-payroll checks and HMRC income verification confirm earnings directly at the source, bypassing applicant-supplied contacts and rogue insiders entirely.
By verifying data directly at the source, Goodlord identifies over 97% of fake employment reference fraud on its platform.
The key takeaway here is to treat a trusted company name on a reference as a starting point, not a conclusion. Just because the organisation is real, that doesn't mean the person vouching for the tenant is telling the truth.
Look closely, and you’ll notice that each type of housing fraud grew by finding a gap that traditional referencing wasn't built to close, whether that's a genuine company with a compromised employee, an accomplice posing as a former landlord, or a synthetic identity engineered to survive scrutiny.
And it won't stop with these three. As agencies adopt stronger verification measures, fraud will continue to migrate toward whatever remains the softest target.
That's why the strongest response isn't one sharper check. It's a layered one. Nishma Parekh, Goodlord's Director of Referencing, frames it as three distinct layers working together:
As Chris Norris, Chief Policy Officer at the National Residential Landlords Association, puts it:
“The market is now falling prey to increasingly sophisticated types of fraud and landlords need to take every step to protect themselves from these risks.”
Ready to strengthen your referencing process? Get in touch with our team.