Applicant Identity Verification
Deep-investigative review of your applicant pipeline. I personally verify the identity, employment history, and risk profile of candidates you flag for closer examination β catching what your ATS, your background check vendor, and your ID verification tools cannot.
What corporations are struggling with:
β’ North Korean IT workers infiltrating Western engineering teams under stolen identities
β’ Proxy interview fraud β one person passes the technical screen, a different person shows up on day one
β’ AI-generated resumes, cover letters, and interview answers that defeat traditional screening
β’ Identity theft applicants using real peopleβs credentials, photos, and employment history
β’ Money mule recruitment into AP, treasury, and executive-support roles β BEC fraud from inside the building
β’ Voice-cloning and deepfake video interviews that pass live video screens
What Iβll do per candidate:
β’ LinkedIn profile authenticity verification β using the same methodology that produced 54,210 fraudulent-job takedowns and 7,000+ fake-profile removals
β’ Cross-platform identity consistency check across LinkedIn, GitHub, X, public records, prior employer footprints
β’ Employment history validation β confirming the candidate worked where they claim, in the role they claim, during the dates they claim
β’ Photo and document forensic review for AI generation, image reuse, and known fraud-pattern markers
β’ Pattern matching against documented fraud-operator typologies (Talentify network, Panzer network, North Korean IT worker indicators, money-mule recruitment patterns)
β’ Risk scorecard with confidence interval and specific red flags identified
Deep-investigative review of your applicant pipeline. I personally verify the identity, employment history, and risk profile of candidates you flag for closer examination β catching what your ATS, your background check vendor, and your ID verification tools cannot.
What corporations are struggling with:
β’ North Korean IT workers infiltrating Western engineering teams under stolen identities
β’ Proxy interview fraud β one person passes the technical screen, a different person shows up on day one
β’ AI-generated resumes, cover letters, and interview answers that defeat traditional screening
β’ Identity theft applicants using real peopleβs credentials, photos, and employment history
β’ Money mule recruitment into AP, treasury, and executive-support roles β BEC fraud from inside the building
β’ Voice-cloning and deepfake video interviews that pass live video screens
What Iβll do per candidate:
β’ LinkedIn profile authenticity verification β using the same methodology that produced 54,210 fraudulent-job takedowns and 7,000+ fake-profile removals
β’ Cross-platform identity consistency check across LinkedIn, GitHub, X, public records, prior employer footprints
β’ Employment history validation β confirming the candidate worked where they claim, in the role they claim, during the dates they claim
β’ Photo and document forensic review for AI generation, image reuse, and known fraud-pattern markers
β’ Pattern matching against documented fraud-operator typologies (Talentify network, Panzer network, North Korean IT worker indicators, money-mule recruitment patterns)
β’ Risk scorecard with confidence interval and specific red flags identified
Deliverables per candidate:
β’ Investigation report (PDF) with executive summary and detailed findings
β’ Risk score: Low β Medium β High β Critical, with the specific evidence behind each tier
β’ Clear recommendation: Proceed / Proceed with additional verification / Decline
β’ Screenshots and evidence archive supporting every flag raised
β’ 24β48 hour turnaround on standard cases; rush available
Pricing:
Single candidate (standard): $1,500
Bundle of 5 candidates: $6,000 ($1,200 each)
Bundle of 10 candidates: $10,000 ($1,000 each)
Bundle of 25 candidates: $20,000 ($800 each) β enterprise rate
Rush 24-hour turnaround: $3,000 per candidate
Executive / C-suite candidate review: $3,500 per candidate
What makes this different:
β’ Human pattern recognition. ATS systems, background-check vendors (HireRight, Checkr), and ID verification tools (Persona, Jumio, Onfido) catch what is catchable by automation. They miss what is designed to defeat automation. I catch what they miss.
β’ Built on real fraud-operator pattern data. 54,210 takedowns of upstream fake-employer fraud means I know exactly what the downstream fake-candidate fraud looks like β the same operators run both sides.
β’ Direct deliverable. No portal, no dashboard, no integration project. You send the candidates. I send the report. You make the decision.
Deep-investigative review of your applicant pipeline. I personally verify the identity, employment history, and risk profile of candidates you flag for closer examination β catching what your ATS, your background check vendor, and your ID verification tools cannot.
What corporations are struggling with:
β’ North Korean IT workers infiltrating Western engineering teams under stolen identities
β’ Proxy interview fraud β one person passes the technical screen, a different person shows up on day one
β’ AI-generated resumes, cover letters, and interview answers that defeat traditional screening
β’ Identity theft applicants using real peopleβs credentials, photos, and employment history
β’ Money mule recruitment into AP, treasury, and executive-support roles β BEC fraud from inside the building
β’ Voice-cloning and deepfake video interviews that pass live video screens
What Iβll do per candidate:
β’ LinkedIn profile authenticity verification β using the same methodology that produced 54,210 fraudulent-job takedowns and 7,000+ fake-profile removals
β’ Cross-platform identity consistency check across LinkedIn, GitHub, X, public records, prior employer footprints
β’ Employment history validation β confirming the candidate worked where they claim, in the role they claim, during the dates they claim
β’ Photo and document forensic review for AI generation, image reuse, and known fraud-pattern markers
β’ Pattern matching against documented fraud-operator typologies (Talentify network, Panzer network, North Korean IT worker indicators, money-mule recruitment patterns)
β’ Risk scorecard with confidence interval and specific red flags identified
Deep-investigative review of your applicant pipeline. I personally verify the identity, employment history, and risk profile of candidates you flag for closer examination β catching what your ATS, your background check vendor, and your ID verification tools cannot.
What corporations are struggling with:
β’ North Korean IT workers infiltrating Western engineering teams under stolen identities
β’ Proxy interview fraud β one person passes the technical screen, a different person shows up on day one
β’ AI-generated resumes, cover letters, and interview answers that defeat traditional screening
β’ Identity theft applicants using real peopleβs credentials, photos, and employment history
β’ Money mule recruitment into AP, treasury, and executive-support roles β BEC fraud from inside the building
β’ Voice-cloning and deepfake video interviews that pass live video screens
What Iβll do per candidate:
β’ LinkedIn profile authenticity verification β using the same methodology that produced 54,210 fraudulent-job takedowns and 7,000+ fake-profile removals
β’ Cross-platform identity consistency check across LinkedIn, GitHub, X, public records, prior employer footprints
β’ Employment history validation β confirming the candidate worked where they claim, in the role they claim, during the dates they claim
β’ Photo and document forensic review for AI generation, image reuse, and known fraud-pattern markers
β’ Pattern matching against documented fraud-operator typologies (Talentify network, Panzer network, North Korean IT worker indicators, money-mule recruitment patterns)
β’ Risk scorecard with confidence interval and specific red flags identified
Deliverables per candidate:
β’ Investigation report (PDF) with executive summary and detailed findings
β’ Risk score: Low β Medium β High β Critical, with the specific evidence behind each tier
β’ Clear recommendation: Proceed / Proceed with additional verification / Decline
β’ Screenshots and evidence archive supporting every flag raised
β’ 24β48 hour turnaround on standard cases; rush available
Pricing:
Single candidate (standard): $1,500
Bundle of 5 candidates: $6,000 ($1,200 each)
Bundle of 10 candidates: $10,000 ($1,000 each)
Bundle of 25 candidates: $20,000 ($800 each) β enterprise rate
Rush 24-hour turnaround: $3,000 per candidate
Executive / C-suite candidate review: $3,500 per candidate
What makes this different:
β’ Human pattern recognition. ATS systems, background-check vendors (HireRight, Checkr), and ID verification tools (Persona, Jumio, Onfido) catch what is catchable by automation. They miss what is designed to defeat automation. I catch what they miss.
β’ Built on real fraud-operator pattern data. 54,210 takedowns of upstream fake-employer fraud means I know exactly what the downstream fake-candidate fraud looks like β the same operators run both sides.
β’ Direct deliverable. No portal, no dashboard, no integration project. You send the candidates. I send the report. You make the decision.