When a job seeker clicks “apply,” the employer and the platform must decide: Is this a real person or a fabricated application? This decision underpins trust in the entire recruitment ecosystem.
Fraudulent or misrepresented applications undermine trust, increase screening costs and waste recruiters' time.
As artificial intelligence tools such as AI checkbecome better at resume writing or impersonation, platforms need to improve their verification system. A recent study found that 38% of HR teams now use AI fraud detection software, and 25% use biometric or facial verifications.
In this article, we'll look at identity verification, document verification, content and behavior analytics, compliance, and new technologies.
Identity verification to prevent impersonation and creation of synthetic profiles


Before diving into credentials, the platform must confirm that the applicant is real. Many systems require:
- Scan a government-issued ID (passport, license) and analyze its fields.
- A live selfie or short video clip that allows you to match your face to your ID using facial recognition.
- SMS or checking email confirm control over contact channels.
- Device fingerprinting and IP reputation to detect reused hardware or anonymous networks.
All of this comes together to form an identity risk assessment. If discrepancies arise, such as a mismatch between a face image and an ID, the system submits the case for manual review.
This layered approach prevents impersonation and synthetic identities (non-existent people created from data).
However, identity verification still needs to take friction into account: too many hurdles can lead to the loss of genuine candidates. Many platforms use progressive gatingdoing minimal checks early on and introducing more serious checks only when anomalies appear.
Verification of credentials and employment history
Once the identity has been pre-verified, the next task is to verify applications: education, work experience, certificates. Methods include:
- OCR parsing and checking the metadata of submitted documents for forgery.
- Integration with databases or credential registries for issuance verification.
- Salary or HR systems integration (with the candidate's permission) to verify employment.
- Direct recommendation or contacting the employer when automated checks indicate uncertainty.
Platforms summarize trust scores based on consistency, document quality, third-party verification, and time.
Applications with gaps, overlapping periods, or unverified credentials are flagged. In highly licensed sectors (engineering, healthcare), a real-time registry check can confirm current license status.
Some platforms also use background checks as an add-on, but be warned: these checks are often error-prone. One study found that more than half of the cases had at least one false-positive error in the preliminary reports.
Source of confirmation | Strength | Limitation |
Credential Database API | Fast, scalable | Incomplete coverage in some regions |
Connecting a payroll and personnel management system | Direct employer data | Candidate permission required, access |
Manual employer verification | Human verification | Time consuming and expensive |
This hybrid approach improves reliability while controlling costs.
Parsing content and catching anomalies in applications


Even if identity and credentials are verified, the content of the application may indicate fraud. Platforms are used:
- Analysis of resumes using NLP models to structure experience, skills, education.
- Checks for consistency across areas (e.g. no duplication of positions, possible promotions).
- AI fraud detectors that detect overly polished or patterned language.
- Consistency of behavior or questionnaire (eg, time spent responding compared to expected norms).
- Scanning previous submissions for plagiarism or similarity.
For example, an AI detector might flag a cover letter that is too consistent across sections or reflects large web corporations. And from a behavioral perspective, a candidate who spends only a few seconds on a question may seem suspicious.
The content analysis level provides story corresponds identity and powers.
These levels help reduce resume fraud, which 44% of respondents admitted to committing in a 2025 survey (specifically, 24% falsified their resumes).
Behavior monitoring and constant verification
The review does not stop once the applicant is shortlisted. Platforms continue verification through:
- Monitored video interviews: lock browser tabs, track eye gaze or face matches.
- Engagement indicators: real users tend to revisit messages, respond to them, and edit submitted materials.
- Correlation of signals between applications: the same device, IP address or writing style for different accounts can indicate fraud networks.
- Post-employment audit: checking whether personality and performance meet the stated requirements.
- Continuous re-validation: For long-term or contract roles, periodic re-validation of credentials or behavior.
These constant levels help identify counterfeiting after hiring or detect anomalies later. Real candidates naturally engage and develop their profiles; scammers often exhibit shallow and boisterous behavior. Monitoring beyond hiring helps curb fraud and recalibrate models over time.
Balancing friction, ethics and regulatory constraints
Rigorous vetting must be combined with principles of fairness, privacy and regulation. Key issues:
- User Friction – Too many steps discourages genuine candidates. Many platforms conduct checks gradually, expanding them only when a risk is detected.
- Bias and fairness – Facial recognition or artificial intelligence models may not work correctly in different demographic groups. Human review and verifiability are essential.
- Privacy and Consent – Laws such as GDPR require explicit consent, data minimization and user rights (access, correction, deletion).
- False positives and disputes – Legitimate candidates may be flagged. Platforms should allow appeals and human review.
- Gaps in coverage – API checks may not cover all regions or institutions. Backup methods (manual) are still needed.
Achieving the right balance ensures trust without alienating real users and compliance without undue interference.
New technologies are changing the applicant screening process


The new frontier is the combination of decentralized identity, blockchain and federated trust. For example:
- Credentials tied to the blockchain allow institutions to issue tamper-proof certificates that can be verified by any validating authority.
- Decentralized Identity (DID) systems allow applicants to pre-verify identity attributes with trusted issuers and then share the evidence with platforms.
- Federated verification networks allow platforms to exchange trust signals (e.g., a candidate has been verified elsewhere).
- Adaptive ML models are constantly retrained on flagged and accepted cases to detect evolving fraud tactics.
These innovations promise reduced friction, shared trust and stronger fraud protection. However, adoption remains limited so far. Implementation issues include standards, infrastructure, and global interoperability.
Final Thoughts
Therefore, validating actual candidate applications on job platforms requires a multi-layered, evolving approach. Identity verification, credential verification, content analytics, behavioral monitoring, compliance, and new trust technologies are all intertwined.
Each layer may be imperfect individually, but together they form a stable network. For platforms competing in hiring quality, implementing these screening systems is no longer mandatory. This is important to protect reputation, reduce waste and maintain trust in the digital recruitment process.