Detect Candidate Fraud in Live Interviews
Identity verification
Candidate behavior
Fraud report ready
LIVE CANDIDATE FRAUD SIGNALS
CANDIDATE FRAUD REPORT · SESSION 110
84 / 100 · Verification review
Multiple identity and behavior signals were captured. A reviewer should inspect the linked moments before deciding.
02:14 · Identity reference changed after join
05:32 · Candidate voice pattern changed
08:47 · Repeated off-screen verification gaps
11:03 · Response behavior diverged
12:31 · Secondary speaker audio detected
Candidate fraud timeline
Signals are linked to timestamps and context so reviewers can verify what happened—not rely on a single score.
Candidate verification · Evidence-led review · Human decision required
CANDIDATE FRAUD RISK SIGNALS
Investigate the signals most associated with remote interview fraud: identity mismatch, proxy participation, off-screen assistance, and responses that no longer match the candidate’s demonstrated profile.
Identity mismatch
Flag visual or behavioral inconsistencies that may indicate the person who joined is not the verified candidate.
Proxy candidate signals
Surface signs of side-channel coaching, secondary speakers, and another person guiding the interview.
Deepfake interview risk
Review anomalies in video presence, lip movement, and identity continuity that may need human investigation.
Identity continuity gaps
Spot abrupt changes in appearance, voice, behavior, or attention that deserve a closer review.
Response authenticity changes
Review answers that suddenly diverge from the candidate’s background, earlier conversation, or demonstrated skills.
Off-screen assistance
Bring context to repeated gaze shifts, hidden notes, and potential external support during remote interviews.
CANDIDATE IDENTITY, NOT ASSUMPTION
Turn candidate fraud concerns into a defensible hiring decision.
When an interview raises a candidate fraud concern, Sherlock AI organizes the relevant identity and behavior evidence into a clear review trail—so hiring teams can investigate fairly before a costly mis-hire.
REAL-TIME VERIFICATION
Confirm who is participating before the interview moves on.
Establish an identity baseline at the start, then surface changes in presence, behavior, or participation as the interview unfolds.
Candidate identity snapshot
VERIFYING
Matched
Face reference
Consistent
Voice profile
Observed
Live presence
Ready
Reviewer evidence
Verification signal pulse
Identity and behavior changes across the interview
EVIDENCE LAYERS
Connect identity, behavior, and participation evidence.
Bring together identity continuity, response patterns, and signs of outside involvement to understand whether the candidate is genuinely present.
REVIEW QUEUE
Review the moments where candidate context shifts.
Jump to the points where identity, attention, or answer behavior diverges from the interview’s earlier baseline.
Linked verification moments
02:14 · Face reference changed after join
08:47 · Repeated off-screen verification gap
12:31 · Voice profile changed mid-session
Open candidate evidence trail →
Candidate fraud handoff
Evidence package ready for review
Identity signals, timestamps, and reviewer notes are organized in one place.
REVIEW READY
Give hiring teams a complete candidate-verification record.
A focused evidence packet links identity observations, participation signals, and reviewer notes—so teams can resolve concerns fairly.
CANDIDATE FRAUD DETECTION CAPABILITIES
1
Detect
Surface identity and behavior signals
2
Verify
Inspect the candidate evidence
3
Decide
Keep the decision human
Real-time candidate fraud alerts
Alert interviewers to meaningful candidate-fraud signals while the interview is still fresh—not after a costly hiring decision.
Evidence-backed identity reports
Give reviewers timestamps, linked identity signals, and explainable reasons to assess a candidate-fraud concern before it affects a hiring decision.
Fits remote hiring workflows
Add Sherlock AI to scheduled interviews with minimal setup and a respectful candidate experience designed for evidence-led review.
Built for candidate identity verification
Support fair remote hiring by surfacing possible proxy candidates, identity mismatches, deepfake interview risk, and external assistance for human review.
THE SHERLOCK DIFFERENCE
From candidate uncertainty to evidence-backed review.
Sherlock gives hiring teams the context to investigate identity and proxy-candidate concerns thoughtfully—before assumptions become decisions.
BEFORE SHERLOCK
A candidate identity concern without context.
Reviewers are left piecing together identity changes, behavior shifts, and incomplete notes after the moment has passed.
• Unclear signals and subjective recall
• Slow, inconsistent investigation
• High-stakes decisions without proof
AFTER SHERLOCK
A clear identity trail to the next step.
Sherlock turns candidate identity and behavior signals into linked evidence, giving every reviewer the context to assess a concern fairly.
✓ Timestamped, reviewable evidence
✓ Faster alignment across reviewers
✓ Fairer, more confident decisions
✓ Fits your existing interview workflow
✓ Human review stays in control
Ready to review candidate risk with clarity?
See how Sherlock turns a candidate identity concern into reviewable evidence.
CANDIDATE FRAUD FAQs
What is candidate fraud detection?
How does candidate fraud detection help verify remote interview identity?
Can candidate fraud detection identify proxy candidates and interview imposters?
Can it help identify deepfake interview risk?
How does it detect external assistance during an interview?
How do candidate fraud reports support fair hiring?
Does candidate fraud detection replace human review?
See how Sherlock AI helps hiring teams investigate identity and impersonation concerns with reviewable context—not automated verdicts.