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The trust layer for hiring

Keep bad actors out without slowing hiring down.

ApplicantX identifies suspicious candidates, AI-assisted deception, and identity fraud before they gain access to your workforce.

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Partners in Trust
The ApplicantX Principle

Fraud is rarely exposed by one check. The pattern tells the story.

01 Individual signals

One signal rarely tells the whole story.

A device anomaly, an identity mismatch, or an unusual interview behavior may be explainable on its own. ApplicantX evaluates signals in context rather than treating any single observation as proof.

02 Connected context

Risk emerges when the signals connect.

ApplicantX connects activity across identities, devices, applications, interviews, references, and candidates to surface inconsistencies and relationships that isolated checks can miss.

The Threats

Candidate fraud doesn’t look like one thing anymore.

Today’s threats span identity, credentials, technology, interviews, and coordinated behavior. ApplicantX is designed to surface patterns associated with each—without treating a signal as a conclusion.

01
Fake & synthetic identities
Fabricated identities, synthetic personas, fake profiles, and applicants who are not who they appear to be.
02
Misrepresented credentials
False or manipulated resumes, employment history, qualifications, experience, or professional claims.
03
AI-assisted deception
Generative AI and other tools used to misrepresent capabilities or improperly assist during hiring.
04
Interview manipulation
Proxy interviewers, candidate swapping, manipulated audio or video, deepfakes, and impersonation.
05
Reference manipulation
Candidates posing as references, shared devices or IPs, contact anomalies, relationship mismatches, and LinkedIn or professional-profile inconsistencies.
06
Coordinated & repeat fraud
People or groups reusing contact data, devices, infrastructure, credentials, or tactics across identities.
The Signals

What ApplicantX evaluates.

A signal may mean very little by itself. ApplicantX becomes powerful when signals are evaluated together—in context and over time.

01

Email address signals

Email reputation, history, domain context, and anomalies associated with the address.

02

Phone number signals

Phone reputation, carrier and location context, and anomalies associated with the number.

03

Resume signals

Employment history, professional claims, resume data, and related inconsistencies.

04

Device & network

Device fingerprint, IP data, VPN or proxy usage, shared infrastructure, and technical context.

05

Digital presence

Relevant public and professional-profile signals that help evaluate consistency with candidate claims.

06

Application behavior

Patterns in how candidates apply, interact, complete steps, and move through hiring.

07

Interview integrity

Signals associated with impersonation, video manipulation, deepfakes, and suspicious interview behavior.

08

Reference check signals

Shared devices or IPs, contact anomalies, relationship mismatches, and suspicious reference activity.

Connected detection layer

These are not additional inputs. They emerge when ApplicantX connects the underlying signals.

Across stages

Identity Consistency

Compares one candidate’s identity and information across applications, interviews, verification, and other hiring moments.

Across candidates

Network Effect

Compares candidates to identify repeated devices, contact details, infrastructure, and patterns within your environment or the opt-in network.

Cross-Stage Consistency

Every step reveals more of the picture.

A moment can look legitimate on its own. ApplicantX keeps checking whether the candidate’s information and identity remain consistent as hiring progresses.

01 Apply
12 Low
  • Resume lightly AI-polished
  • Email + phone checks clear
  • Digital presence consistent
02 Screen
24 Low
  • Deepfake · low confidence
  • AI-generated answers · low confidence
03 Assess
38 Review
  • High-risk VPN connection
04 Interview
57 Review
  • AI-generated answers · medium confidence
  • Interview image differs from earlier captured image
  • Deepfake · low confidence
05 References
74 Elevated
  • Multiple references use the same device
  • Reference activity shares candidate infrastructure
06 ID Check
87 Elevated
  • Selfie + ID image inconsistent with previously captured candidate images
One living candidate fraud profile
From fragmented signals to a connected, explainable picture.
Group threat detection

Stop the group. Not just the applicant.

Connect shared identities, devices, networks, and tactics to reveal the broader pattern behind one suspicious applicant.

01

Detect the connection

Link seemingly unrelated applicants through shared signals.

02

Map the infrastructure

Surface the devices, identities, and networks behind the activity.

03

Surface the next attempt

Recognize related patterns when they appear again.

Fraud-Network
The ApplicantX Network

You need a network to defeat a network.

Fraud rings reuse infrastructure because creating new identities, devices, accounts, and digital artifacts takes effort. ApplicantX recognizes those relationships—first within your environment and, when you choose to participate, across the ApplicantX Network.

Your data. Your controls.
A network when you want one.

The broader ring becomes available only when an organization chooses to participate.

applicantx-network-email-match-final-transparent
Participating employers do not browse one another’s candidate databases. Exact network and privacy language should be confirmed with Product and Legal before publication.
Fraud suspicion rating Elevated

Review recommended

3 connected findings contributed to this assessment.

Identity inconsistency Interview and verification attributes do not align
Shared device detected Device previously associated with another identity
Contact artifact reused Related signal appears across multiple candidates
Explainable by design

A rating is only useful if you understand what drove it.

ApplicantX shows what was detected, which signals contributed, how they relate, and why the applicant was surfaced for review.

No unexplained black-box conclusion
Underlying findings visible to reviewers
Human judgment remains in control
Deployment

Fraud defense without rebuilding your hiring stack.

Use ApplicantX as a centralized review environment or embed relevant signals and assessments into established hiring workflows.

01 Platform
Applicant review queue Monitor · investigate · resolve

ApplicantX Platform

A centralized environment for fraud detection, monitoring, investigation, and review.

02 API
POST /applicant/assess
rating: "elevated"
findings: [ ... ]
200 OK

ApplicantX API

Bring fraud signals and assessments into existing hiring systems and workflows.

Responsible connected detection

Suspicion is a signal. Not a verdict.

ApplicantX connects fraud-related signals into an explainable assessment, then leaves investigation and action in human hands.

01

Collect

Evaluate signals generated throughout the hiring lifecycle.

02

Connect

Identify inconsistencies, anomalies, repeated artifacts, and cross-candidate patterns.

03

Assess

Translate relevant findings into an explainable Fraud Suspicion Rating.

04

Act

Let your organization review, route, escalate, or resolve the applicant within its workflow.

ApplicantX surfaces potential fraud for review. Your organization decides what happens next.

A Brilliant company

Built for better hiring.

ApplicantX is the hiring fraud defense technology within the Brilliant company portfolio.

The hiring platform
Interviews + reference checks
Behavioral + cognitive science
Hiring fraud defense

Know who you're letting into your company.

See ApplicantX in Action