Intelligence Dashboard
Fraud & Identity Verification Landscape
What 40,000+ public Reddit posts reveal about fraud and identity verification · January 2025 onwards
Reddit Data PipelineIntelligence Dashboard
What 40,000+ public Reddit posts reveal about fraud and identity verification · January 2025 onwards
Reddit Data PipelineDataset
Analyzed 10,834 public Reddit posts that discuss or mention identity verification, collected from January 2025 onwards.
Top Communities
Total IDV Posts
10,834
Identity verification discussions
#1 Friction Type
False Rejection
2,535 posts
Negative Sentiment
88.7%
Of all IDV discussions
Top Platform
Roblox
857 posts
What exactly goes wrong during verification, and which methods cause the most friction?
What goes wrong during identity verification
False rejection dwarfs every other friction type. This is the core tension: every rejected legitimate user is a potential customer lost.
Distribution of verification types encountered
Biometric methods (selfie, liveness, facial age estimation) are color-coded separately. These represent the most technically demanding verification steps.
Why are users being asked to verify in the first place?
Why users encounter identity verification
Understanding what triggers verification reveals whether friction comes from onboarding (fixable with better UX), suspicious activity flags, or periodic rechecks (a policy problem).
Which platforms are generating the most friction?
Top 10 platforms by IDV discussion volume, with their primary friction type
Each platform's top friction type reveals its specific IDV failure mode. False rejection dominates: legitimate users locked out of their own accounts.
Three emerging patterns that signal where IDV is heading →
Three signals shaping the IDV landscape
Age gates are the new verification frontier. Platforms are rolling out age estimation and document checks, creating a fresh wave of friction for legitimate users.
Drivers and couriers face repeated identity checks that interrupt their livelihood. Uber, Lyft, and DoorDash are among the most-mentioned platforms for verification failures.
Legitimate users are being locked out of their own accounts. False rejections erode trust and push users toward workarounds that weaken security posture.
The data tells one story. The users tell another.
What real users say when verification fails them
“Trying to set up a bank account, and the sign up sheet doesn’t accept my (birth) name as a valid name.”
“Opened a ticket providing the required info 3 days ago and got zero reply as of yet. Thanks discord!”
“IT HAS NOTHING TO DO WITH PROTECTING CHILDREN FROM ADULT CONTENT, AND EVERYTHING TO DO WITH CONTROL AND CENSORSHIP OF INFORMATION, AND TRACKING OF PEOPLE.”
“If steam account is older than age verification requirements it should bypass question everytime.”
“For remote, we may ask them to hold up their government issued ID.”
“I'm 28. I added my bank card trying to verify. I had no idea it specifically NEEDED to be a credit card. I do not have a credit card. And I do not want one. So as you can tell I'm extremely pissed off.”
Liveness checks verify a person is physically present, not a photo, video, or deepfake. But our data shows real friction in selfie and liveness flows. The opportunity: verification systems that are both harder to spoof and easier for legitimate users.
Document verification + selfie matching + liveness detection form the modern identity verification stack. Getting this right means reducing false rejections while maintaining defense against presentation attacks.
False rejection is the dominant friction type. Legitimate users are being told they're not who they say they are.
Improving document parsing, supporting name variants and non-Latin scripts, and better face-matching across appearance changes would recapture legitimate users currently being locked out.
Gig platforms show the highest reverification friction. Drivers and couriers face repeated identity checks that interrupt their livelihood.
Adaptive risk scoring could reduce unnecessary re-checks while maintaining security, reserving full reverification for genuinely suspicious activity.
Many posts describe being locked out with no alternative method, a single point of failure in the verification flow.
Offering fallback verification paths (manual review, alternative document types, video-assisted verification) prevents permanent lockout and recovers users that automated systems reject.
Age verification is a growing compliance wave, with platforms rapidly rolling out age estimation and document checks.
Facial age estimation that balances compliance requirements with minimal false rejection for legitimate users is a fast-growing product opportunity, especially as age gates expand beyond gaming and social media.