Every hiring decision carries risk, and those risks multiply exponentially year over year. Between AI-generated resumes that perfectly mirror job descriptions and deepfake candidates conducting entire interviews under false identities, employers face an unprecedented challenge: distinguishing genuine talent from sophisticated fabrications.
The financial impact is staggering, and the threat is no longer theoretical—it’s happening right now, across every industry.
The True Cost of Getting It Wrong
When we talk about “bad hires,” we’re not just discussing someone who underperforms. We’re talking about a cascading financial disaster that impacts every corner of your organization.
The U.S. Department of Labor estimates that a bad hire costs at least 30% of the employee’s first-year earnings. Some of these costs accumulate through Direct Financial Losses — recruitment expenses, onboarding costs, training investments, and salary payments—all wasted on someone who can’t deliver. Then there’s team disruption—research from SHRM found that 95% of CFOs said a poor hiring decision impacts team morale, with 35% saying morale is greatly affected, triggering a domino effect of resignations!
The Deepfake Crisis: A New Dimension to Hiring Risk
If traditional bad hires are costly, fraudulent candidates represent an entirely different level of threat—one that combines financial loss with cybersecurity risks and potential legal liability.
The numbers are alarming:
- Gartner predicts that by 2028, one in four job candidates globally could be fake.
- 17% of U.S. hiring managers encountered candidates using deepfake technology in video interviews (Resume Genius survey, March 2025)
- Two-thirds of hiring professionals in a 2025 survey said they support mandatory ‘live-only’ interviews as a way to verify candidate identities
How to Spot AI-Polished Resumes and Fake Candidates
Before candidates even reach the interview stage, there are red flags that can help you identify potentially fraudulent applications:
1. Overly Perfect Keyword Matching
AI-generated resumes often mirror job descriptions too precisely, using identical phrases and technical terms in unnatural ways. Real candidates describe their experience in their own words, even when they closely match your requirements.
2. Generic Achievement Descriptions
Watch for accomplishments that sound impressive but lack specific metrics, project names, or contextual details. Genuine candidates naturally include specifics when discussing their work—”increased sales by 40% through implementation of new CRM system” rather than “significantly improved team performance.”
3. Inconsistent Technical Depth
AI-generated resumes may list advanced skills but fail to demonstrate interconnected knowledge or show the natural progression that genuine technical experience would reveal. For instance, someone claiming 5 years of Python experience should demonstrate familiarity with the language’s evolution and its ecosystem.
4. Missing Digital Footprint
Deepfake candidates often use AI to create fake LinkedIn profiles that appear real but lack critical information, such as employment history, or have very little activity or few connections, Fortune.
5. Suspicious Video Interview Behaviors
During virtual interviews, watch for:
- Audio and video that don’t sync properly
- Unnatural facial movements or lighting inconsistencies
- Refusal to perform simple actions like waving a hand in front of their face
- Delays between lip movements and speech
Built-In Integrity & Authenticity Verification
This is where Prequel LENS directly addresses the challenge of deepfakes and AI fraud. Every assessment includes an Integrity Report that monitors:
- Identity & Session Verification: Confirms candidate identity and session validity
- Copy/Paste & Keystroke Analysis: Monitors paste activity and typing behavior for authenticity tracking
- AI Misuse Detection: Identifies use of generative tools during the assessment
- Focus Tracking: Flags when attention drifts away from the assessment
- Complete Action Timeline: Logs every test action—navigation, code edits, pauses, and time stamps
This multi-layered verification ensures you’re not just evaluating technical skills—you’re confirming the person taking the assessment is who they claim to be and is completing the work themselves.
The ROI of Early Verification
Consider the math: If the average cost of a bad hire is about $17,000 so, do the math! More importantly, early verification:
- Saves interviewer time by ensuring only qualified, verified candidates reach your team
- Reduces time-to-hire by identifying top performers quickly
- Protects against fraud before bad actors gain access to your systems
- Improves quality of hire by focusing on demonstrated ability rather than polished presentations
- Provides defensible hiring data, should any decision be questioned
Taking Action
The hiring landscape has fundamentally changed. AI tools have enabled anyone to create compelling resumes and conduct convincing interviews under false pretenses. By 2028, experts predict one in four candidates could be fake, according to Gartner.
The companies that will thrive in this environment are those that adapt their verification processes before making costly hiring mistakes—not after.
Ready to protect your hiring process from fraudulent candidates while identifying genuine top talent?
Learn more about Prequel LENS and our digital verification process
