Estimate the cost of bad hires and your potential savings with IntegrityLens.
Direct cost of turnover & rehiring
By preventing fraud with IntegrityLens
Preventing even one bad hire pays for the platform.
Verify candidate identity and technical skills before you hire.
Start Free VerificationMaking the wrong hiring decision is expensive. The U.S. Department of Labor estimates that the average cost of a bad hire is up to 30% of the employee's first-year earnings. For a $100,000 technical role, that's $30,000 lost—and that figure is often understated.
According to SHRM research, when you factor in recruitment fees, onboarding costs, lost productivity, and the impact on team morale, the true cost can reach 50-60% of annual salary. For senior engineering or leadership positions, this can mean $75,000-$150,000 per mistake.
The situation worsens when interview fraud is involved. With the rise of AI tools like ChatGPT and real-time deepfake technology, candidates can now misrepresent their skills or even their identity. Studies show that 31% of hiring managers have encountered candidates using false identities or proxy interviewers.
Research from SHRM and the U.S. Department of Labor suggests bad hires cost between 30-60% of the employee's annual salary. For a $100,000 role, expect to lose $30,000-$60,000 per bad hire when factoring in recruitment costs, training, lost productivity, and replacement expenses.
When candidates use AI tools, proxy interviewers, or deepfakes to pass interviews, you're hiring based on fabricated skills. These hires inevitably fail to perform, increasing your bad hire rate. IntegrityLens detects these fraud signals during the interview itself, preventing the problem upstream.
Our platform verifies candidate identity through biometric liveness detection, monitors for AI assistance during coding assessments, and flags suspicious behavior like proxy interviews. By catching fraud before you extend an offer, you avoid the downstream costs entirely.
This varies by role and industry. For fully remote technical roles with unproctored assessments, 20-30% is realistic. For in-person roles with strict verification, 5-10% is more typical. Use our Deepfake Risk Assessment to evaluate your specific vulnerability.