Hiring has never moved faster: resumes screened in seconds, interviews scheduled by chatbots, offers extended within days. But the same automation that compressed the funnel has created an opening on the other side of the table. As generative AI makes it trivial to fabricate a polished resume, a synthetic voice, or a face that passes a video interview, HR leaders are discovering that speed and certainty are starting to trade off against each other.
The findings behind the shift
HireRight’s 2026 Global Benchmark Report, based on survey responses from more than 1,900 human resources, risk, and talent acquisition professionals worldwide collected between February 18 and March 6, 2026, found that accuracy and quality of results remain the top priority employers weigh when picking a screening provider. But layered underneath that priority is a newer and less comfortable one: organizations reported growing concern around identity fraud, AI-enabled hiring risks, and workforce trust, which the report ties directly to rising adoption of identity verification checks and post-hire screening programs worldwide.
The report also found that most companies uncovered candidate discrepancies during background screening in the past 12 months, with employment verification the area most likely to surface inconsistencies, and enterprise-sized organizations reporting the highest discrepancy rates of all. In EMEA and APAC specifically, misalignment between a candidate’s behavior or history and company values has overtaken the traditional “cost of a bad hire” as the top risk employers are trying to screen against.
“As organizations navigate rapid advances in AI, increasing workforce fraud risks, and continued hiring challenges, the findings in HireRight’s 2026 Global Benchmark Report provide valuable insight into how employers around the world are adapting their screening, hiring, and workforce management strategies,” said Euan Menzies, HireRight’s president and CEO, in the report’s release. “We’re pleased to share these findings to help organizations benchmark their programs and make more informed workforce risk management decisions.”
Why the fraud side of the ledger is filling up faster
The HireRight data lines up with a wider body of research pointing the same direction. Gartner has separately warned that by the end of 2026, roughly 30% of enterprises will find their standard identity-verification tools can no longer reliably distinguish a real face from a deepfake, as synthetic-identity kits circulating online now bundle a real Social Security number with a fabricated face and resume so that individual checks come back clean. In a 2026 Gartner survey, 59% of hiring managers said they suspected candidates of using AI to misrepresent themselves during the process, a jump from a July 2025 Gartner survey in which 6% of 3,000 job candidates admitted to participating in interview fraud, either posing as someone else or having someone else pose as them.
The detection side is starting to catch up, unevenly. InCruiter, an interview-technology vendor, said that after launching deepfake-detection tooling in early 2026, it found fraudulent activity in 25% to 30% of sessions its system flagged as suspicious, nearly double the rate that experienced human interviewers had previously caught on their own. That gap between what algorithmic detection surfaces and what a human recruiter would catch unassisted is itself becoming an argument for building verification into the pipeline rather than treating it as a background-check afterthought.
None of this is happening in a vacuum of AI adoption. HireRight’s report found AI adoption in HR continuing to grow overall, though attitudes toward candidates using generative AI tools during the application process vary sharply by region: North American respondents were largely neutral or unsure, while nearly half of APAC respondents viewed candidate AI use positively. That regional split matters, because it means there is no single emerging norm HR teams can borrow; each organization is having to decide for itself where AI-assisted applying ends and AI-enabled impersonation begins.
What it means for the HR leader
The practical challenge is that identity verification is moving from a late-stage compliance step to a front-of-funnel requirement, and most screening infrastructure was not built for that. A background check that runs after an offer is extended does nothing to stop a fabricated candidate from getting through a video interview or a technical assessment in the first place. Talent acquisition leaders are having to push verification earlier, often before a recruiter invests real time in a candidate, which changes both the tooling budget and the candidate experience teams have to design around.
It also raises the stakes on vendor selection. As AI-driven hiring and layoff tools increasingly become a source of litigation exposure, an identity-verification product that misfires, whether by wrongly flagging a legitimate candidate or missing a fabricated one, carries legal as well as reputational risk. HireRight’s finding that misalignment with company values is overtaking “cost of a bad hire” as the top screening concern in EMEA and APAC suggests employers are already widening the definition of what a background check needs to catch, beyond simple credential fraud into behavioral and cultural fit signals.
The report’s other headline trend compounds the problem: organizations globally reported declining plans to hire fully remote employees in 2026, with rising expectations for office-based or hybrid arrangements. Read together with the identity-fraud findings, that shift looks less like a return-to-office preference and more like a hedge, since a candidate who has to show up in person is harder to fabricate than one who only ever appears on a video call.
What to do now
HR and talent acquisition leaders evaluating their screening stack should treat identity verification as a pipeline-stage decision, not a final gate: move a lightweight verification check earlier in the funnel, before significant recruiter or hiring-manager time is spent, rather than only at offer stage. Screening vendors should be asked directly what detection rate they achieve on synthetic or AI-assisted fraud attempts and how that is validated, not just what their overall accuracy claim is. And any organization already navigating degraded signal quality elsewhere in AI-mediated hiring should treat identity fraud as compounding, rather than separate, from that broader signal problem: both point to the same underlying issue, that the tools built to speed up hiring were not built to verify who is actually on the other end of it.
Source: HireRight
