AI-generated payslips, employment records, and identities are making tenancy fraud increasingly difficult for landlords and agents to detect. This warning follows an analysis by tenancy platform Goodlord, which reviewed over one million tenant references submitted across two years.
The analysis revealed that fraudsters are moving beyond individual forged documents. Fake employment references saw a 226.6% increase during 2025, making it the fastest-growing type of detected fraud. Referee fraud rose by 146.4%, and identity manipulation increased by 140.4%.
Nishma Parekh, Goodlord's director of referencing, stated that fraudsters are now building entire fake identities, complete with false employers and invented referees, rather than relying on single forged payslips. This sophistication means that no single verification method is sufficient on its own.
Goodlord found that 41 applications in every 1,000 references were flagged for suspected tenancy fraud between July 2025 and June. The firm estimates that a fraudulent tenancy could expose landlords to an average direct financial loss of £9,601 through arrears, property damage, void periods, and possession costs. Applying this suspected fraud rate across an estimated 5.3 million privately rented households suggests a potential annual exposure of up to £4.1 billion.
London recorded confirmed fraud rates nearly double the national average, the highest in the UK, followed by the West Midlands and the North West. Properties with rents exceeding £10,000 a month showed confirmed fraud rates approaching 18 in every 1,000 applications, which is three to six times the rate for average properties.
Chris Norris, chief policy officer for the National Residential Landlords Association, urged landlords to be vigilant. He advised that landlords should immediately contact the police and Action Fraud if they suspect they have been victims of fraud. He also stressed the need for referencing checks to evolve in response to fast-moving technological developments, including regular reviews of systems used to assess applications.