EY India’s analysis of more than one million background checks across 90-plus mid- and large-sized organisations found that experienced professionals accounted for 96% of identified fraud cases in healthcare, 88% in financial services and 79% in IT/ITeS. That composition is the opposite of what most hiring teams assume.
The fresher with the suspiciously clean CV has a company now, and most of that company has been in the workforce for years. Companies that treat senior-hire verification as a formality because the candidate has a decade on paper carry the largest share of the risk.
What the Data Says About Who Commits Fraud
The EY findings cut against assumptions that still shape BGV spend in Indian companies: that fraud is a fresher problem, and that senior candidates self-regulate because they have reputations to protect. The 2025 report, The First Firewall, is the largest Indian dataset on hiring fraud currently in public view, and it tells a different story about where the risk actually sits.
| Sector | Share of fraud cases involving experienced professionals |
| Healthcare | 96% |
| Financial Services | 88% |
| IT/ITeS | 79% |
Separately, HireRight’s 2026 Global Benchmark Report, based on a YouGov survey of HR and risk decision-makers, found 74% of Indian employers detected identity fraud among candidates or employees during 2025. That rate placed India first across every market the research covered, ahead of the Middle East at 60%, Southeast Asia at 45%, and the UK at 24%.
The two datasets measure different things. EY looks at discrepancies surfaced during actual background checks. HireRight measures what HR teams report having caught. Both point in the same direction: credential fraud in India is a volume problem, and that volume increasingly includes people with established careers.
“What I also find these days is that fraud now is not necessarily a fresher problem,” said Aashii Chutani, Assistant Vice President – HR & OD at Unified Vision Capital, who has watched the composition shift from inside the verification process. “EY India’s 2025 analysis of more than one million pre-employment screenings across 90+ organisations found that experienced professionals accounted for 96% of identified fraud cases in healthcare, 88% in financial services and 79% in IT/ITeS. So, the assumption that someone with 10 years of experience must have a genuine profile can be a dangerous one.”
What Senior-Hire Misrepresentation Looks Like in Practice
Fraud at the experienced end is less about forged degrees and more about documents that are technically accurate but materially inflated. The hardest discrepancies to flag are the ones where every underlying fact is partly true, which is why Aashii describes the problem as sitting on a spectrum rather than a line.
“I think the first thing to recognise is that credential fraud isn’t always a completely fabricated story. Sometimes, it is a creative truth,” she explained. “A candidate may have worked in an organisation but inflate their designation, tenure, salary, team size or responsibilities. Someone who was a Manager may present themselves as a Senior Manager. Someone who managed three people may imply they led a 30-member team. And sometimes a six-month stint quietly becomes a year on the CV.”
The forged letter leaves fingerprints. The upgraded designation does not. That difference matters because document authentication tools catch one and miss the other entirely. The counterintuitive part is that the cruder frauds are easier to investigate than the subtle ones.
“Then there are the more obvious cases of fake experience letters, altered salary slips, forged degree certificates, fake institutions or qualifications,” Aashii added. “I find the grey area interesting; i.e. the completely fake degree may be easier to investigate; however, exaggerations made here & there are harder to spot.”
Hidden Employment Histories
Remote and hybrid work have widened the opportunity for candidates to omit or compress jobs that overlap. A candidate who held two concurrent roles during the pandemic can surface only the one that fits the current application.
“I also see another layer emerging of dual employment and hidden employment histories, particularly in an environment where remote and hybrid work have made traditional visibility more difficult,” Aashii noted.
TPB’s moonlighting in India coverage has tracked how companies respond when existing employees take on parallel work. The inverse pattern, where candidates hide that parallel work from a future employer, is harder to see because the records are often incomplete.
AI-Doctored Documents
Document fraud used to leave visible fingerprints: inconsistent fonts, poor alignment, mismatched logos. Generative tools have narrowed that gap. A fake experience letter now reads and looks correct enough to pass a routine visual check, which erodes one of the oldest signals verification teams relied on.
“And AI is changing the game too,” Aashii said. “A document that once looked suspicious because of poor formatting can potentially be made to look remarkably authentic using readily available technology.”
Experienced-hire BGV packages tend to lean more heavily on document verification and less on direct employer calls, which is exactly where the AI-generated artefact lands cleanly.
Why Senior-Hire Verification Thins Out
The structural reason senior fraud goes undetected sits inside hiring process design rather than inside BGV tools. Verification depth tends to be inversely related to the candidate’s apparent credibility, and seniority is often read as credibility.
Compounding dynamics are familiar: time pressure on offer rollout, the assumption that a long employment history is self-validating, and reference-call protocols that stop at HR-to-HR confirmation instead of probing peers or former reports about scope and ownership. TPB’s piece on what BGV tools catch and miss in Indian hiring laid out why reference gaming is a growing problem at this level, with well-prepared candidates seeding reference trees with sympathetic names.
Even where BGV is deployed thoroughly, there’s a ceiling on what any tool can resolve on its own.
“A tool can flag an inconsistency. It can authenticate a document or identify that information doesn’t match. It cannot understand the context behind the discrepancy,” Aashii explained. “For example, is it a genuine administrative / typing error? Has the previous employer changed its designation structure? Is there an internal transfer involved? Or is the candidate deliberately misleading the prospective employer?”
That ceiling is also where HR’s own value shows up. Final judgement on a discrepancy is a human call, and treating BGV output as the decision instead of as an input is where senior-hire fraud lives.
What Changes About Verification Depth for Senior Roles
Senior-hire verification can’t rely on the same document-centric playbook used for frontline hiring. The depth of the check needs to match the depth of the claim, and that means pulling levers that most Indian BGV packages treat as optional add-ons.
Vendor selection follows the same logic. Speed and cost per check are the wrong primary filters when credential fraud is the actual concern.
“As a Sr. HR & OD Expert, I wouldn’t choose a BGV platform simply because it offers the fastest turnaround time or it’s the most economical,” Aashii said. “If credential fraud is the primary concern, I would look for depth of verification rather than speed or cost.”
Her working framework for depth covers source verification, document intelligence, cross-checking, anomaly detection and human escalation.
“A good solution, according to me, should cover these five key areas:
a) Strong source verification: Can it verify information directly with employers, universities, and other professional bodies, rather than simply relying on documents submitted by the candidate?
b) Document intelligence: Can it identify altered, manipulated or suspicious documents? With AI making sophisticated document creation easier, this is very important.
c) Cross-checking: Can the BGV system compare information and highlight inconsistencies?
d) Anomaly and fraud detection: The future of BGV is not just mentioning ‘verified’ or ‘not verified’. It is about identifying pattern or highlighting anomalies that deserve a human intervention.
e) Human escalation: I strongly feel that a red flag shouldn’t automatically become a rejection by prospective employer. It should become a question. I have seen cases when a discrepancy has a legitimate explanation. HR should take the opportunity to understand it before making a hiring decision,” Aashii laid out.
What follows walks through the areas where senior-hire fraud sits most heavily.
Source Verification Over Document Verification
The core shift moves from “the candidate submitted a letter” to “the employer confirmed the role as stated”. Verification is most useful when it operates as a straight comparison exercise.
“This is where a good BGV system becomes really useful,” Aashii offered. “At its simplest, employment verification is a comparison exercise, i.e. what did the candidate tell us, and what does the employer’s record say?”
The principle plays out as mismatches in dates, designations and institutional claims.
“A verification process can check employment-related information (depending on the process, applicable consent, & privacy requirements) such as employment dates, designation, reason for leaving, etc.,” Aashii described. “For example, if a candidate says they worked at Company A from January 2021 to December 2023, but the previous employer confirms March 2021 to September 2023, the system flags the mismatch. The same principle applies to education. A candidate may claim a particular degree, specialisation or institution, while verification checks whether the qualification was actually awarded and whether the institution is recognised.”
Direct employer calls neutralise the AI-generated letter problem by surfacing inflation at source. TPB’s BGV process and DPDP compliance guide covers how direct verification sequences typically run under India’s consent framework.
Cross-Referencing That Catches Context Drift
Designation changes across employers, internal transfers and reorganisations create legitimate reasons a title can look inflated. The verification layer should connect dots across the CV, letters and education records as a set, rather than treat each document in isolation.
“The real value of modern technology here is when it can connect the dots,” Aashii explained. “A strong BGV platform can bring together document verification, database checks, identity matching, employment and education verification, and anomaly detection to identify patterns that may not be obvious when each piece of information is looked at separately.”
The illustration is a cross-document problem rather than a single-document failure.
“For example, the problem may not be that one document is fake. It could be that the dates across the CV, experience letter and education record don’t logically fit together. Technology here can be particularly used to help HR connect the dots at scale,” Aashii acknowledged. “But I would be careful about saying that a BGV tool can detect everything. It can’t.”
Human Judgment at the Red-Flag Stage
Treating every discrepancy as an automatic rejection creates a different problem. Legitimate candidates with messy records get screened out, and the hiring team loses the chance to understand what the anomaly actually means. The purpose of BGV is better hiring decisions, which is a different outcome from auto-rejection.
“That’s an important distinction because BGV is ultimately about making sound hiring decisions and not being an automated rejection machine,” Aashii noted.
Together, these shifts change the vendor evaluation question. The criteria for selecting a BGV platform look different when experienced-hire fraud is the primary concern: depth of source verification, document intelligence against AI-generated artefacts, and the quality of the escalation workflow matter more than turnaround time or cost per check. A tool worth buying, by that definition, operates as an intelligent flagger rather than a verdict machine.
In the End…
The experienced-hire fraud pattern suggests Indian HR teams need to redistribute verification depth. Pulling intensity from the frontline screening pool to fund deeper checks on mid- and senior-level hires closes the biggest gap the EY data reveals.
Practical adjustments to that depth include adding at least one peer or reporting-line reference call for every senior offer, requiring direct employer verification on all claimed roles above a defined grade, and giving hiring managers a 48-hour window to review and contextualise any flagged discrepancy before BGV is formally closed. Technology can confirm what the record says. Verifying what the record means is a hiring-team job.
FAQs
Who commits most credential fraud in Indian hiring?
Experienced professionals, not freshers. EY India’s 2025 analysis of over one million background checks found experienced hires accounted for 96% of fraud cases in healthcare, 88% in financial services and 79% in IT/ITeS.
How common is identity fraud among Indian candidates?
HireRight’s 2026 Global Benchmark Report found 74% of Indian employers detected identity fraud among candidates or employees during 2025, placing India first globally, ahead of the Middle East at 60% and the UK at 24%.
What does senior-hire credential fraud usually look like?
Rarely a fully fabricated story. More often it is designation inflation, exaggerated team size, stretched tenure, or salary bumps on an otherwise real role. The underlying facts are partly true, which is why routine document checks miss them.
Why is AI making document verification harder?
Generative tools can produce experience letters and salary slips without the formatting giveaways that once flagged forgeries. That erodes a signal verification teams relied on, especially in senior-hire packages that lean on documents over direct employer calls.
What should a BGV platform cover for senior hires?
Five areas: source verification with employers and institutions, document intelligence against AI-generated artefacts, cross-checking across records, anomaly detection, and human escalation. Depth matters more than turnaround time or cost per check.
Should every red flag lead to rejection?
No. A discrepancy may stem from an administrative error, a changed designation structure, or an internal transfer. HR should treat flags as questions to investigate, not automatic rejections, and give hiring managers a window to review context before closing the check.
