False Positive Flags in Coding Tests: A Support-Grade Playbook

It is 9:40 AM. A finalist for a remote role fails an integrity check right after a strong coding assessment. The candidate emails your shared inbox with screenshots, tags the CEO on LinkedIn, and asks for their data deletion in the same thread. Meanwhile, the recruiter is asking Support to "just override it" to keep the onsite schedule. If you treat this like a one-off, you will either (a) reject an honest candidate and create churn plus reputational risk, or (b) waive controls in a way that teaches bad actors which flags are bluffable. Support needs a consistent, fast, defensible script: what to check, what to request, what to retake, when to escalate, and how to close the loop in the ATS.

Default decision: do not override, do not reject. Move the case into a step-up flow that (1) confirms identity continuity, (2) validates assessment integrity, and (3) documents the rationale in an Evidence Pack. This preserves speed because most honest false positives clear through a bounded retake or a quick manual review, and it preserves security because high-risk patterns still escalate with standardized evidence, not vibes.

Checkr reports that 31% of hiring managers say they have interviewed a candidate who later turned out to be using a false identity. Directionally, this implies identity fraud is common enough that you need consistent gating before interviews, not just after an incident. It does not prove your industry, role type, or funnel has the same rate, and it does not tell you which detection signals are most reliable in your stack. Pindrop reports that 1 in 6 applicants to remote roles showed signs of fraud in one real-world pipeline. Directionally, remote hiring increases the attack surface and raises the need for automation plus evidence-based review. It does not mean 1 in 6 are confirmed fraud, and it does not tell you the false-positive rate of any single control.

Recommendation: assign one accountable owner for the decision, and keep Support out of the role of "fraud judge." Support should run intake and evidence collection, then route by policy. Ownership model that works in practice: - Recruiting Ops owns the policy (risk tiers, retake eligibility, SLAs) and the candidate-facing process. - Security owns the high-risk escalation criteria and approves any new signals or step-ups. - Hiring managers own the hiring decision only after integrity is cleared to an interviewable state. What is automated vs manually reviewed: - Automated: flag generation, risk tier assignment, gating actions (block, step-up, allow), creation of an Evidence Pack shell, ATS status updates via idempotent webhooks. - Manual: medium-risk adjudication, retake approval, and high-risk escalation review with Security. Sources of truth: - ATS is the system of record for stage and disposition. - Verification service is the system of record for identity continuity evidence (document, face, voice, liveness results). - Assessment platform is the system of record for code telemetry and proctoring signals. - IntegrityLens Evidence Pack is the system of record for the decision narrative and artifacts tied to the candidate.

Use these definitions internally so Support, TA, and Security stop arguing about vocabulary mid-incident.

Recommendation: treat every flagged-but-verified case as a two-question decision: (1) is identity continuous, and (2) is the assessment attempt attributable and consistent. Step 1: Freeze the state, not the candidate - Lock the current attempt artifacts (verification result, assessment telemetry, timestamps). Do not ask the candidate to "try again" before you snapshot evidence, or you lose comparability. - Ensure the ATS reflects "Step-up required" rather than "Rejected" to avoid reputational damage if the case clears. Step 2: Classify the flag into a Support-friendly bucket - Identity continuity mismatch (name/DOB mismatch, face mismatch, liveness quality issues). - Assessment integrity anomaly (tab switching spikes, copy/paste bursts, impossible timing, multiple faces in frame). - Environment anomalies (VPN/proxy, device fingerprint volatility, unusual geo change). Step 3: Apply bounded step-ups (do not debate intent) - For identity-quality issues: offer a one-tap retake with clearer lighting instructions and a quality threshold. Keep it time-boxed (example: within 24 hours) so scheduling stays predictable. - For environment anomalies: require a step-up verification before the next interview stage, not a rejection. Ask for a second factor that is hard to spoof, not a long explanation email thread. - For assessment anomalies: require an attributable retake in a stronger mode (stricter proctoring or a short live verified follow-up) rather than accepting a narrative. Step 4: Review with an evidence rubric, not gut feel - Confirm whether the same verified identity appears across attempts (document, face, voice) and whether liveness passed at adequate quality. - Compare attempt fingerprints: device, network, timestamp patterns, and assessment telemetry. Look for consistency, not perfection. - Decide: Clear, Step-up again (one time), or Escalate to Security. Step 5: Close the loop with a candidate-safe explanation - Never accuse. Use neutral language: "We could not verify continuity on this attempt" or "We need an additional verification step to proceed." - Provide the next action in one sentence, with an ETA and an appeal path. Step 6: Write the Evidence Pack note as you go - Support adds intake context, candidate communications, and which policy clause was applied. - Reviewer adds the specific artifacts consulted and the decision rationale. - Recruiting Ops ensures the ATS stage and disposition match the decision, then closes the ticket.

This example policy is designed so Support can route cases consistently, while Security retains control of high-risk escalation. Customize thresholds to your environment and validate with your Legal and Security teams.

IntegrityLens AI is the first hiring pipeline that combines a full Applicant Tracking System with advanced biometric identity verification, AI screening, and technical assessments. For false positives, it gives you Risk-Tiered Verification controls, Evidence Packs, and workflow automation so Support can resolve flags fast without informal overrides. Used by TA leaders, recruiting ops, and CISOs, IntegrityLens supports: - ATS workflow as the system of record for stage and disposition - Identity verification in under three minutes (typical end-to-end document + voice + face in 2-3 minutes) before interviews - Fraud detection signals that trigger step-ups and review queues - 24/7 AI screening interviews to reduce scheduling pressure during step-ups - Coding assessments in 40+ languages with integrity telemetry and defensible rubrics

• "Just override it" exceptions in Slack with no Evidence Pack, which creates audit findings and teaches attackers that persistence wins. - Unlimited retakes without stronger verification, which lets fraudsters A-B test your controls. - Zero-tolerance auto-reject on any anomaly, which inflates false positives and pushes good candidates to competitors.