The Rise of Fake Applicants, And How Recruiters Can Catch Them Before It’s Too Late

To catch a thief, you have to think like one.

That’s the mindset we adopted at Brainner as we dove deep into one of the fastest-growing problems in recruiting today: fake applicants.

As resume screening software becomes more automated and remote roles become more accessible, fake candidates are slipping through the cracks — wasting recruiters’ time, compromising hiring quality, and even putting companies at risk.

So we decided to study them. Closely. (Okay, we may have gotten a bit obsessed 😅)

🎭 The 3 Archetypes of Fake Applicants

Here are three fake applicant “types” we’ve uncovered, and yes, they’re getting more sophisticated:

The Liar

They use their real identity but fabricate their experience.

Fake jobs, fake skills, fake degrees — all made to look legit.

Usually their resume is “too perfect,” and they often over-qualify for entry-level jobs to ensure callbacks.

The Impostor

They steal a real person’s resume, usually someone qualified and experienced.

They keep the work history and achievements but swap out the name, email, and phone number — trying to land a job using someone else’s identity.

This is especially common in remote tech roles, where verification steps may be weak.

The Spammer

They create dozens of fake profiles with slight resume tweaks and apply to the same role multiple times.

The goal? Flood the pipeline and hope at least one gets through the initial screen.

Often tied to fraud rings or coordinated scam attempts.

But Why Are There Fake Applicants in the First Place?

The answer varies — but here are some of the leading hypotheses:

Multi-job fraud: People trying to hold 5–10 remote jobs simultaneously and only “appear active” without doing actual work.
Outsourcing scams: A person gets hired and then outsources the job to someone else (often cheaper labor or automation).
Geopolitical manipulation: Some cybersecurity experts point to state-sponsored actors (e.g., North Korea) trying to infiltrate Western tech companies.
Visa/immigration fraud: Individuals fabricating experience to meet job requirements for sponsorship.
Pure financial motivation: People aiming to collect onboarding bonuses, equipment, or first paychecks — and then disappear.

The motivation may differ, but the impact is the same: it’s wasting recruiter time, damaging trust, and blocking real talent from being seen.

🧠 How to Detect Fake Applicants (Without Going Crazy)

While fake applicants are getting smarter, there are still red flags you can spot — especially when using the right combination of tools and human insight.

Here are practical checks to include in your screening process:

🔗 LinkedIn Red Flags

No contacts / no activity at all
New profile (e.g. created 2023) with a 15-year work history
Suspiciously perfect profiles with vague descriptions
Broken or missing employment links — profile doesn’t appear in the employee list of the company they claim
URL with random characters (typical of autogenerated or throwaway profiles)
No photo or a photo that appears AI-generated

📞 Phone Number Signals

VoIP or virtual numbers that don’t match the candidate’s claimed location
Same number used across multiple applications with different names
Numbers that fail basic online searches

📧 Email Address Anomalies

Strange or mismatched domains
Big mismatch between the applicant’s name and the email address

(e.g. “maria.garcia” applying with “johnsmith44288@…” — huge red flag)

Repeated patterns indicating mass application tools

📄 Resume Anomalies

A resume that looks too perfect or “AI-generated polished”
Copy-paste sections that show up on Google (stolen profiles)
Repeated formatting issues indicating the document was patched together
Achievements too vague or identical across multiple roles
Senior-level responsibilities for junior experience

🔍 How Brainner Helps Detect Fake Applicants at Scale

Let’s face it — checking all those red flags manually is a full-time job:

Cross-referencing LinkedIn profiles, Googling phone numbers, checking email patterns, scanning resumes for AI-generated fluff… it’s a lot.

That’s exactly where Brainner comes in.

We’ve built our resume screening software to automate these checks at scale, so you can focus on making decisions — not digging through noise.

Here’s how we help:

Automated red-flag detection

Brainner continuously monitors for signals like:

Inconsistent resume timelines
Mismatched emails and names
Suspicious phone number reuse
Overqualification for junior roles
Missing LinkedIn profiles or newly created accounts

First-party fake applicant database

We maintain a proprietary database of known fake applicant patterns (shared across our client network), so we can cross-check every new resume against previously flagged behavior.

You stay in control

We don’t reject candidates automatically. Instead, we raise the red flags, give you all the context, and let you make the final call.

Brainner acts as your AI co-pilot, helping you catch what humans often miss — without losing control, transparency, or speed.