Detect fake candidates before they reach your pipeline

Applicant fraud detection is the process of identifying fraudulent, fake, or misrepresented job applications before they reach a recruiter's review queue, flagging risky candidates before interviews are ever scheduled. Brainner cross-references each applicant against 3.5 billion data points and 20+ fraud patterns in real time, and surfaces high-risk profiles with the specific signals that triggered the alert. Recruiting teams using Brainner stop wasting interview slots on fake candidates and focus their time on people who are genuinely qualified and genuinely real.

Brainner integrates with your ATS and cross-references every applicant against 3.5 billion data points and 20+ fraud patterns the moment they apply. By the time a recruiter opens a profile, the fraud risk signal is already displayed alongside the screening score: no batch processing, no delay, no manual trigger required.

Applicant fraud in hiring is any attempt by a candidate to misrepresent their identity, credentials, or profile to gain an unfair advantage during recruitment. It includes AI-generated resumes, synthetic identities, impersonating another person's identity, proxy interviewers, coordinated fraud campaigns, and state-sponsored schemes targeting remote tech roles.

No. Brainner flags candidates as high risk and surfaces the specific signals that triggered the alert. It does not make autonomous rejection or blocking decisions. Your team reviews the flags and decides whether to reject or proceed. This ensures full human oversight while keeping your process compliant with security, data protection, and AI regulations (including GDPR, CCPA, the EU AI Act, and more).

Brainner integrates natively with Greenhouse, Lever, Bamboo HR, Workday, iCIMS, Ashby, SmartRecruiters, Workable, JazzHR, Recruitee, and Zoho Recruit. Fraud detection runs the moment candidates apply, no manual trigger required.

Don't see your ATS? Contact us, we can likely integrate it.

Yes. Brainner is SOC 2 Type II certified and designed to comply with GDPR, CCPA, and emerging AI regulations including the EU AI Act. Brainner performs ongoing AI bias audits to ensure the system does not introduce discrimination against protected groups. Fraud detection signals are strictly advisory: Brainner does not make autonomous decisions or take actions on candidates. Your team remains fully in control of all hiring decisions.

North Korean IT worker fraud is a state-sponsored scheme in which operatives use stolen identities, VPNs, and fabricated credentials to obtain remote tech jobs, funneling paychecks back to weapons programs. The FBI, CISA, and OFAC have issued formal warnings to US companies. Brainner detects the application patterns, identity inconsistencies, and location signals associated with these fraud rings before they advance in your pipeline.

Deepfake interview fraud uses AI-generated video or audio to impersonate a real candidate during a remote interview, masking the true identity of the person on screen. Brainner flags behavioral and identity signals at the application stage that indicate deepfake risk, so recruiting teams can apply additional verification before advancing a candidate to an interview.

Brainner's fraud detection model was trained on over 1 million applicants across companies in Tech, Fintech, Healthtech, and more, learning the patterns that reveal fraud across 20+ distinct fraud types. The model cross-references each applicant against 3.5 billion data points from public and proprietary sources in real time. Rather than applying a single rule, Brainner runs multiple AI signal layers simultaneously: identity verification, resume pattern recognition, behavioral analysis, and network cross-referencing, and surfaces a composite risk score with the specific signals that triggered each flag.