Brainner detects fake applicants using a two-step approach: first, criteria-driven AI screening filters applications down to qualified candidates. Then, the Identity Check Report runs automatically on those qualified profiles, flagging high-risk applicants before recruiters review them. This works integrated with Greenhouse, Lever, Workday, Recruitee, SmartRecruiters, Workable, JazzHR, Zoho Recruit, iCIMS, BambooHR, Ashby and other major ATS platforms.
Remote tech roles see 30-50% fraud rates. But adding manual verification steps (LinkedIn stalking, upfront reference calls) slows hiring and loses real candidates to faster competitors. Brainner's approach catches fraud without adding friction: you only verify candidates who are already qualified.
Learn more about Brainner's AI resume screening.
The Hidden Cost of Fake Applicants in Remote Tech Hiring
Fake applicants create real risks: security breaches from fraudulent hires, compliance violations, and pipeline pollution that breaks your metrics and pushes away genuine talent. The time wasted is just the visible cost.
The Scale of the Problem
Remote tech roles see fraud rates between 30-50% according to Brainner pipeline data from 2025-2026. For a typical remote Data Analyst role with 300 applications, that means 90-150 fake profiles mixed into your pipeline. Without detection, recruiters spend an average of 45+ minutes reviewing each fake profile before realizing something's wrong.
The Friction Trap
Most companies respond to fraud by adding manual verification steps: cross-referencing LinkedIn profiles to check if the person exists and if the employer lists them, Googling phone numbers to verify area codes, checking email patterns for mismatches or burner accounts, and inspecting resume metadata for generation tools or suspicious creation dates. Recruiters become detectives before they become interviewers.
LinkedIn verification for every applicant? That's 5-10 minutes per profile.
Phone number checks? Another 3-5 minutes.
Email validation, social media searches, metadata inspection combined? Another 5-10 minutes. Reference calls before first interview? 10-15 minutes.
Add it all up and you're looking at 25-45 minutes per candidate. Multiply that by 300 applications and you're burning 125-225 hours of recruiter time per role. And while you're verifying fakes, the best real candidates accept offers from faster competitors.
This sometimes solves the fraud problem but creates a new one: slow time-to-hire that loses you real talent.
Brainner's Two-Step Approach to Fraud Detection
Brainner runs fraud detection after screening, not before. This means you only invest verification resources on candidates who are already qualified, not the entire pipeline.
Why Order Matters
Unlike other fraud detection tools, Brainner's two-step approach works differently: screen first to identify qualified candidates (based on the criteria you set), then run identity verification only on those top 30. You catch fraud without slowing down your pipeline or adding steps for real candidates.
Step 1: Criteria-Driven Screening (Filter the Good Ones)
Brainner's AI resume screening evaluates all applications against your custom criteria. This includes technical skills match (specific languages, frameworks, tools), experience level (years in role, seniority progression), industry background, role-specific requirements, and work authorization and location.
Output: 300 applications ranked by fit. Top 30 advance to verification.
This works integrated with your ATS platform. Recruiters see ranked candidates in their normal workflow.
Customizable Criteria
The criteria are fully customizable. You define what "qualified" means for each role. Need 3+ years of Python experience? Brainner filters for that. Want candidates with healthcare industry background? That becomes part of the ranking. Looking for specific certifications? Add them to the criteria. The AI ranks every candidate against your requirements.
How This Works with Your ATS
Brainner works integrated with Greenhouse, Lever, Workday, Recruitee, SmartRecruiters, Workable, JazzHR, Zoho Recruit, iCIMS, BambooHR, Ashby and other major ATS platforms. It doesn't replace your ATS - it adds AI screening and fraud detection on top, so your workflow stays exactly the same.
Step 2: Identity Check Report (Find the Genuine Ones)
The Identity Check Report runs automatically on the previously qualified 30 candidates. It cross-references contact verification (email domain age, phone carrier history, deliverability), profile consistency (LinkedIn vs. resume alignment on dates, titles, companies), location verification (phone country matches candidate location, IP patterns), behavioral signals (application timing, automated generation signs), and more. Each check appears as a line item in the candidate's Identity Check section - either validated with a green checkmark or flagged with a warning icon and specific context.
Output: 25 genuine candidates + 5 flagged as "High Risk"
"I was terrified a 'black box' would filter out the best talent. Two weeks in, I'm eating my words. Brainner surfaces the best matches and lets me fine-tune the profile weights myself. The fraud tool is a total lifesaver for flagging AI-padded resumes before they hit my already crowded calendar. Less noise, zero wasted interviews, and my time back."
Devin Ontiveros, Senior Talent Partner at Scan
The High Risk Flag
Brainner doesn't auto-reject. It flags with context. The recruiter sees this in the candidate profile, and the final decision is theirs:
- "High Risk: Location spoofing detected (VPN from sanctioned region)"
- "High Risk: Synthetic identity signals (email created 10 days ago, no digital footprint)"
The recruiter decides whether to proceed. The flag provides context, not a decision.
What the Identity Check Actually Detects
Brainner's Identity Check looks for signals that manual review misses: device fingerprints, contact history, cross-platform inconsistencies, and patterns linked to known fraud operations.
1. Synthetic Identity Fraud
Real employment data + fabricated contact details = synthetic identity.
Example: Resume shows legitimate work history at Google (2020-2023), Senior Engineer title. But the email was created 2 weeks ago, phone number has no carrier history, LinkedIn profile was created 3 months ago (not 3+ years), and there's no GitHub, no Stack Overflow, no professional digital footprint.
A real Senior Engineer who worked at Google for 3 years would have digital traces. This profile doesn't.
2. Location Spoofing
VPNs, proxies, and GPS tools to fake geographic location.
Example: Application claims Austin, Texas. But the IP address resolves to Southeast Asia, device timezone doesn't match claimed location, and multiple applications from same device show different "home addresses."
This matters especially for companies with compliance requirements around hiring from sanctioned regions.
3. Profile Inconsistencies
Discrepancies across LinkedIn, resume, and application data.
Example:
- LinkedIn: "Senior Software Engineer at Amazon, 2020-2024"
- Resume: "Lead Architect, Amazon Web Services, 2019-2025"
- Application email: Personal Gmail created last month
Which timeline is real? If the candidate can't keep their own story straight across platforms, something's wrong.
4. Behavioral Patterns
Mass application tools, script-based submissions, bot-driven profiles.
Some signals Brainner detects:
- 50+ applications submitted in 10 minutes (humanly impossible)
- Identical resume structure with minor text variations
- Applications from same device using different "identities"
- Copy-pasted answers to screening questions across multiple profiles
These patterns indicate automated fraud, not individual applicants.
"Before Brainner, resume review would take hours, and even then, we knew that great talent was getting buried in the process. With their fake applicant detection tool, we can confidently move forward knowing that the candidates we're speaking with are legitimate. It has saved us time, reduced risk, and allows us to focus on connecting with the right candidates."
Lauren Fisher, Senior Manager of Talent Acquisition at IMO Health
The 4 Types of Fraud Applicants
Not all fake applicants use the same tactics. Brainner's framework identifies four distinct types of fake applicants, each leaving different signals.
Read the full breakdown of each type → 4 types of fake appplicants
Why This Framework Matters
Each type leaves different signals. Liars show timeline mismatches. Fakers have no digital footprint. Impostors trigger contact verification failures. Frontmen show device inconsistencies between application and interview.
Brainner's Identity Check is trained to catch all four.
Why the Two-Step Approach Works
By running fraud detection only on qualified candidates, Brainner catches fraud without slowing down your pipeline.
No Friction Added
Candidates don't see extra steps. The application process inside your ATS stays the same: apply, complete any screening questions, submit. Brainner ranks them by fit, then verifies the top candidates. By the time a recruiter opens a profile, the High Risk flag (if any) is already there.
The Integration Advantage
Brainner syncs bidirectionally with your ATS through native integrations. When the Identity Check flags a candidate:
1. A "High Risk" tag appears in the ATS candidate profile
2. Detection details get added to application notes
3. The flag shows up before the recruiter opens the profile
Recruiters don't change their workflow.
Real Results: How Teams Use This
Companies using Brainner's two-step approach report 40-60% reduction in fake applicants reaching recruiter review, without adding time to their hiring process.
Case Study Snapshot
Before Brainner:
- 300 applications per remote tech role
- Approximately 120 fake profiles mixed into pipeline
- Recruiters spent 8-10 hours per role on initial review
- 3-4 fake candidates made it to phone screens (wasted interview time)
After Brainner:
- 300 applications → 30 qualified → 5 flagged High Risk
- Recruiters review 25 genuine candidates only
- Initial review time: 2-3 hours
- Zero fake candidates in phone screens
Time saved: 5-7 hours per role × 10 roles per quarter = 50-70 hours recruiter time saved
"Fraud in recruiting is a real problem: fake candidates, inflated profiles, wasted time. Brainner makes the screening process more reliable and trustworthy. And beyond the features, their support team is genuinely outstanding: fast, human, and always helpful whenever I have a question."
Thayná R., Technical Recruiter at G2i
What This Means for Hiring Velocity
That's 50-70 hours that can go toward interviewing real candidates, building relationships with top talent, or improving the candidate experience. It's also faster time-to-hire because you're not wasting days reviewing fake profiles or conducting phone screens with fraudulent applicants.
If you want to try Brainner >>> Book a demo with us
Continue reading
The 4 Types of Fake Applicants: How to Identify and Detect Each One
There are 4 types of fake applicants: Liars, Fakers, Impostors, and Frontmen. Each poses different risks and requires different detection methods.
The Rise of Fake Applicants, And How Recruiters Can Catch Them Before It’s Too Late
Fake applicants are flooding ATSs with stolen, fabricated, or mass-generated resumes, making it harder for recruiters to find real talent. Brainner automates the detection of red flags—like mismatched emails, fake LinkedIn profiles, and repeated phone numbers—so recruiting teams can catch fakes at scale and stay focused on top candidates.
Save up to 40 hours per month
HR professionals using Brainner to screen candidates are saving up to five days on manual resume reviews.