Multi-Accounting Prevention
Multi-accounting prevention.
Protect access and revenue.
New email addresses can hide repeat accounts claiming the same benefits. ShieldLabs detects multi-accounting and bots so trials, credits and paid benefits are not claimed again through extra accounts.
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Your next project starts here.
Anti-Detect BrowserProxy
Protect introductory offers
Keep trials, credits and discounts from being claimed through fresh accounts.
Make restrictions stick
Help stop linked registrations from bypassing account-level access limits.
Keep acquisition genuine
Keep extra accounts from inflating new-customer and campaign numbers.
Focus your investigation
Filter detected multi-accounting in the dashboard by account count.
Multi-accounting detected
Stop repeat claims.
Protect access limits.
ShieldLabs detects multi-accounting among users registered with different email addresses and bots at signup to help stop repeated claims and restriction bypasses. Visitors remain recognizable across new sessions, cleared cookies and IP changes.
Detection filters
Filter detected activity.
Focus on account count.
Multi-accounting detection starts at three accounts per visitor. Filter detected activity in the dashboard by account count without changing the detection rules.
Expose masked account farms
Detect proxies and anti-detect browsers around repeated registrations.
Protect established accounts
Receive separate account takeover detections around customer access.
The ShieldLabs platform
More than account links.
A complete view of abuse.
Bring ready-made multi-accounting detection together with identity, device and network intelligence, bot detection, risk assessments and traffic quality analytics.
Enterprise-level functionality. Without enterprise pricing.
Accurate identification
Recognize returning visitors even when they clear cookies, use incognito mode or change their IP address. Detect linked users with different email addresses.
Identification accuracy
High-Risk Event detection
Detect account abuse out of the box by automatically identifying linked users. No fraud model training is required.
Risk signals
Get ready-made detections of automation, masking and spoofing, with named risk signals behind each result.
Bot & AI traffic detection
Detect bot activity from browser automation signals. Recognize useful crawler and AI traffic alongside abuse detections.
Risk scoring
Score every visit from 0 to 100 and classify it as trusted, suspicious or dangerous. See the signals behind each score.
Device fingerprinting
Recognize devices by their fingerprints. Identify the browser, operating system and device type. Detect headless browsers and tampering.
IP & Network Intelligence
Reveal the real IP address and geolocation behind masking. Detect VPNs, proxies and private relays.
203.0.113.42198.51.100.23Traffic quality scoring and risk analytics
Get one score for all your traffic and ready-made quality results by source, channel, referrer and UTM campaign.
Analytics dashboard
See detected multi-accounting.
Protect access and incentives.
See detected multi-accounting, associated accounts and risk assessments. See the sources bringing repeated registrations and account abuse.
How it works
Start protecting your product.
Connect once and start receiving ready-made fraud and abuse detections.
- 01
Add the snippet or SDK
Connect your product and start identifying visitors and users.
- 02
Receive ready-made detections
Get detected fraud and account abuse in the dashboard or through API integrations.
- 03
Prevent fraud and abuse
Protect customer accounts, paid access and signup incentives in your product.
Developers
Integrate in
5 minutes.
A single API to identify, detect and score. Receive risk data through API responses and webhooks. Integrate selected data points into your business logic to prevent fraud and abuse.
Protect your users and product
Make abuse visible.
Keep genuine users moving.
Start Free FAQ
Understand the evidence.
Put it to work.
What is multi-accounting?
Multi-accounting is the use of several accounts by the same participant to repeat benefits, bypass restrictions or misuse paid access.
How does ShieldLabs detect multi-accounting?
ShieldLabs detects associated registered accounts, including accounts created with different email addresses. Detection starts at three accounts per visitor. Filter detected results in the dashboard by account count.
Can I filter detections by account count?
Yes. Detection starts at three accounts per visitor. Dashboard filters can narrow displayed detections by account count without changing the detection rules.
What does a multi-accounting detection include?
The result identifies detected multi-accounting and linked user accounts. It is available with the identification’s risk assessment and named signals.
Can different email addresses hide the account relationship?
Account labels can differ while identified visits and other evidence link the activity. Email addresses in the example are supplied by your product.
How do I use the results?
Get linked accounts and ready-made detections in the dashboard or through supported API integrations. Results are ready to integrate with access and benefit workflows.
Can this help when restricted users return with another account?
Yes. ShieldLabs detects multi-accounting among accounts created with different emails. Bot and masking detections also help protect restricted signup and access flows.
Can it detect account farms and fake signups?
For account farm detection, ShieldLabs returns ready-made multi-accounting and bot detections at signup. These expose repeat accounts and automated visits. Fake account detection here does not mean verifying a person’s legal identity.