What is new account fraud?

New account fraud is when one person creates multiple accounts to claim benefits they have already used or to bypass a restriction. It looks legitimate to standard signup checks because every account uses a different email and IP. Abuse-motivated: repeatedly claiming free trials, welcome bonuses, or referral payouts across fresh accounts.; Evasion-motivated: returning after a ban or restriction under a new identity.; Common surface: SaaS trials, marketplace promos, iGaming bonuses, and crypto airdrops. ShieldLabs assigns a persistent identifier on every visit that lets you spot the same person creating new accounts, regardless of email, IP, or cleared cookies.

What are the two types of new account fraud?

The two types of new account fraud are abuse-motivated and evasion-motivated. Both produce the same underlying signal: one persistent visitor behind multiple accounts. Abuse-motivated: opening fresh accounts to repeatedly claim free trials, bonuses, or referral payouts.; Evasion-motivated: returning after a ban or restriction under a new identity to regain access. ShieldLabs links related accounts back to one persistent identity and exposes pre-built patterns indicating both behaviors, so your team can act on the signal that fits each case.

How does ShieldLabs detect fake accounts at signup?

ShieldLabs detects fake accounts at signup by assigning a persistent identifier on the first visit and recognizing the same person when they return under a new email, IP, or browser. The first risk score is available on that first visit. Persistent identification: holds across cleared cookies, rotated IP, and incognito mode.; 100+ signals: evaluated across device, OS, browser, IP, and network layers, including VPN, proxy, anti-detect browser, and other anonymity signals.; Pre-built patterns: correlate related accounts, indicating multi-accounting with a risk level (Suspicious or Dangerous). ShieldLabs returns a 0-100 risk score with a full signal breakdown on every visitor or user, delivered via dashboard, API, and Webhooks so your team can act before the account is created.

How does fake account prevention work?

Fake account prevention works by identifying the person behind each signup before any verification email is sent, then letting your team decide what to do. It starts at the signup form, not after the account already exists. Identify: ShieldLabs returns a persistent visitor identifier and a 0-100 risk score on every registration.; Correlate: related accounts are linked back to one identity through pre-built patterns indicating multi-accounting.; Decide: your code sets thresholds to step up verification or block the session. ShieldLabs gives your team the signals and patterns to build these rules; the block-or-allow decision always stays in your code.

Will ShieldLabs automatically block fake signups?

No. ShieldLabs blocks nothing on its own; it returns the data and your code makes the decision. This keeps you in full control of false positives and lets you tune rules to your own traffic. You receive: a 0-100 risk score, a full breakdown of anonymity signals, and pre-built patterns indicating abuse.; You set the rules: for example, a higher score can step up to phone verification, and a very high score can block account creation.; You test first: the free tier includes 5,000 identifications to validate thresholds on real traffic before enabling rules in production. ShieldLabs surfaces and scores the risk so your team can enforce its own thresholds, rather than handing decisions to a black box.

How is this different from email validation or CAPTCHA?

Email validation and CAPTCHA check whether a single signup looks human and reachable, but they have no memory of who signed up before. ShieldLabs adds persistent identity, so a returning person is recognized across signups. Email validation / CAPTCHA: verify the current session in isolation; a fresh email or solved challenge passes every time.; ShieldLabs: assigns a persistent identifier that survives cleared cookies, rotated IP, and incognito mode. ShieldLabs links a person's third account back to their first as the same visitor, regardless of email address, IP, or cleared cookies, which one-time checks cannot do.

What is multi-accounting?

Multi-accounting is one person creating and operating multiple accounts on the same platform to gain benefits a single account would not get. Each account looks legitimate to standard verification because the email, IP, and browser differ. What it enables: abuse of free trials, referral programs, welcome bonuses, and plan limits.; Why it's hard to catch: no single signup looks suspicious on its own; the pattern only appears when accounts are connected. ShieldLabs lets your team identify multi-accounting by linking related accounts to one persistent identity and surfacing a pre-built pattern indicating many accounts behind a single visitor.

What is fake account fraud?

Fake account fraud is when someone registers accounts with fabricated or recycled identities to abuse platform benefits, bypass restrictions, or manipulate metrics. Because the identity details are disposable, the only reliable signal is the visitor's persistent identity and connection. Fabricated identities: new emails and details per account to claim benefits again.; Recycled identities: banned or flagged users returning under a new persona.; What stays constant: the underlying device, browser, and network characteristics. ShieldLabs assigns a persistent identifier on every visit and surfaces patterns indicating fake account creation when the same person registers under a new identity.

How to detect multi-account fraud during signup?

Detect multi-account fraud at signup by linking each new registration to a persistent identity that holds across cleared cookies, rotated IP, and incognito mode. When the same person opens a second or third account, the connection becomes visible even though every account uses different details. Identify: assign a persistent identifier on the first visit, with the first risk score on that visit.; Correlate: analyze 100+ device, OS, browser, and network signals to connect related accounts.; Flag: a pre-built pattern indicating multi-accounting fires with a risk level (Suspicious or Dangerous). ShieldLabs runs this in real time on every visitor or user, so your team can act on the pattern before a duplicate account is approved.

What is free trial abuse?

Free trial abuse is when the same person repeatedly opens new accounts to claim a trial period they have already used. It is one of the most common forms of new account fraud on SaaS and subscription platforms. Why it slips through: email validation and IP blocks see each fresh signup as a new user.; What it costs: repeated free usage, inflated trial metrics, and lost conversions to paid plans. ShieldLabs links every signup to a persistent identity and surfaces the pattern indicating repeat trials, so your team can require verification or block the account via API before the new trial starts.

Account Fraud Prevention

Stop new account fraud at signup

Prevent promo abuse, fake signups, and multi-accounting without adding friction for real users, through persistent identification and real-time risk scoring

Start Free

Prevent incentive fraud

Block bonus, promo, and referral farming across multiple fake accounts

Prevent trial fraud

Stop free trial abuse and block repeat attempts before another cycle begins

Prevent ban evasion

Keep banned users from bypassing restrictions and returning under a new identity

Distinguish anonymous from real users

Legitimate signups go through without extra friction or added verification steps

How ShieldLabs prevents account fraud

ShieldLabs recognizes the real visitor behind every anonymous session, so your team can stop fraudulent account creation in real time

Accurate Identification

Identify returning visitors and users across sessions, cleared cookies, rotated IP and incognito mode

Anonymity Signals

Detection of VPN, proxy, Tor, anti-detect browser, geolocation spoofing, IP reputation and other anonymity signals on every visit, with up to 99% accuracy

Risk Score

A ready-to-use score reflecting the risk of each visit, with the weight of every signal behind it

Ready-Made Patterns

Detection of the hidden connections between accounts that may signal fake account creation, multi-accounting, and more

Real-Time Analytics

See how much of your traffic is masked, with an overall quality score and a breakdown of the sources sending your visits

Prevent revenue loss and wasted marketing budget

New account fraud prevention for every platform

SaaS & Subscriptions

Stop free trial cycling and plan limit abuse from the same person opening account after account

Marketplaces & E-commerce

Catch referral farming, welcome bonus abuse, and promo fraud before payouts hit your books

iGaming & Online Gambling

Spot bonus abuse, affiliate fraud, multi-accounting, and banned players returning under a new identity

Crypto & Web3

Defend crypto airdrops and reward campaigns from sybil abuse and farming activity

Start preventing new account fraud in 5 minutes

Easily integrate into any signup or onboarding flow

1
Create your account
Get 5,000 free identifications
2
Add the snippet
It identifies every visitor and returns their anonymity signals and a risk score
3
Check your traffic quality
See how much of your traffic is masked, with an overall traffic score
4
Use the API and Webhooks
Apply the risk score and anonymity signals in your rules to stop fraud and abuse