What fraud and abuse do SaaS companies face?

SaaS (Software as a Service) companies face a cluster of abuse types that all start with one person creating accounts they should not have. The common patterns are: Free trial abuse and trial cycling, where one person recreates a trial after the last one expires; Multi-accounting, where one person runs many free accounts in parallel; Fake signups and account farms, where accounts are created in bulk to harvest free resources or referral rewards; Promo abuse, where discount and referral codes are claimed repeatedly; Plan or usage-limit bypass, where new accounts are created to dodge the limits of a paid tier. ShieldLabs surfaces patterns indicating one person behind many accounts, using a persistent identifier that works across sessions regardless of email, IP, or cleared cookies.

How does fake-account abuse hurt a SaaS business?

Fake-account abuse costs a SaaS business far more than the free-tier infrastructure it consumes, it corrupts the funnel data the whole team plans against. The damage lands in three places: Distorted metrics, where fake and farmed accounts inflate signups and activation, so trial-to-paid conversion, retention curves, and cohort analysis are quietly wrong and budget gets allocated against bad numbers; Wasted acquisition spend, where every fake signup attributed to a paid channel raises the apparent cost of a real customer; Consumed resources, where free-tier infrastructure, support time, and referral payouts are spent on accounts that were never going to convert. ShieldLabs flags the fake account on the first visit, before it enters the funnel, so both the metrics and the spend stay tied to real users.

How does ShieldLabs detect trial abuse and multi-accounting?

ShieldLabs links each signup to the person behind it with a persistent identifier, so it surfaces a returning trial abuser or multi-accounter even when the email, IP, and cookies are all new. A single JavaScript snippet on the signup, login, or trial activation page returns: a 0-100 risk score with a signal breakdown; a persistent identifier that ties the new signup to any prior account from the same person; anonymity signals such as VPN, proxy, Tor, anti-detect browser, and IP reputation; active patterns in analytics (multi-accounting, account farms, and other patterns). When a new signup matches an identity already linked to a prior account, the matching pattern fires. ShieldLabs delivers the risk score and signals via API and Webhooks, so your code can hold, limit, or downgrade the account before the trial is activated.

Does ShieldLabs replace email verification or payment-fraud tools?

No, ShieldLabs works at the device and identity layer and runs alongside email and payment checks, not instead of them. The three layers cover different gaps: Email verification confirms an address is real, but not that one person controls fifty of them; Payment-fraud tools watch the card and the transaction, not the device behind the account; ShieldLabs links accounts to the same person across sessions, adding the multi-accounting layer the other two miss. ShieldLabs does not validate email addresses, send SMS codes, or screen payment cards, most SaaS teams keep those checks in place and add ShieldLabs for the identity layer.

How is this different from CAPTCHA and rate limiting?

CAPTCHA and rate limiting stop scripts and bursts, but neither one recognizes a returning person; ShieldLabs links a new signup to a prior account even when the email, IP, and cookies are all new. The difference by layer: CAPTCHA slows scripted signups but does nothing about one real person opening fifty accounts by hand, and is solvable or bypassable; Rate limiting caps signup speed from one IP, but an abuser on rotating residential IPs stays under every limit; ShieldLabs adds a persistent identifier that ties the new account to the same person regardless of email, IP, or cleared cookies. ShieldLabs is the layer that tells you a new account is not a new person, keep CAPTCHA and rate limiting and run it alongside them.

Can ShieldLabs detect account sharing?

Yes, ShieldLabs surfaces account sharing by spotting multiple distinct devices and visitors using one account, which is the inverse of multi-accounting. The two directions: Multi-accounting, which is one person on many accounts (trial cycling, farms); Account sharing, which is many people on one account, common when a paid SaaS seat is shared across a team or resold. ShieldLabs surfaces the account sharing pattern with the degree of suspicion, so a SaaS team can prompt an upgrade, split the seat, or apply its own policy, it reports the pattern, and the decision stays with you.

How do I integrate ShieldLabs into a SaaS product?

Integration takes about 5 minutes: add one JavaScript snippet to your signup, login, or trial activation page, and the first risk score arrives on the first visit. The steps: Install the JavaScript snippet on your signup, login, or trial activation page; Pass an optional UserHID (a hashed identifier for the user's account) after the account is created; Read risk score, anonymity signals, and patterns via REST API and Webhooks for server-side flows. ShieldLabs includes 5,000 one-time identifications on the free tier with no credit card required, so you can test on real traffic before enabling production rules.

Will ShieldLabs automatically block signups?

No, ShieldLabs blocks nothing automatically; it returns a risk score and a full signal breakdown, and your code decides what to do. You set the thresholds, for example: A higher-risk score can trigger email or phone verification before the trial is provisioned; A top-of-range score can hold the signup for review or route it to a restricted trial; Lower-risk signups pass through with no extra friction. ShieldLabs includes 5,000 identifications on the free tier so you can test thresholds on real traffic before enabling rules in production.

SaaS Fraud Prevention

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it drains your revenue

Keep trials, free-tier resources, and conversion metrics tied to
real users, and stop one person from farming a hundred accounts

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Trust your funnel metrics

Keep activation, conversion, and retention free of fake accounts

Protect your free tier

Keep trials and free-tier resources for real users, not farms

Protect paid-tier revenue

Stop multi-accounting and account sharing from replacing paid plans

Keep real users friction-free

Legitimate users sign up and start without extra verification steps

How ShieldLabs protects SaaS products from abuse

ShieldLabs recognizes the real visitor behind every anonymous session, to flag one person behind many accounts before a fake signup enters the funnel

Accurate Identification

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

Anonymity Signals

Detection of VPN, proxy, Tor, anti-detect browser, 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 multi-accounting, account farms, account sharing, 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

Stop abuse on your SaaS product in 5 minutes

Easily integrate into any signup, login, or trial activation flow

1
Create your account
Sign up and 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 API & Webhooks
Apply the risk score and anonymity signals in your rules to stop fraud and abuse

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api.shieldlabs.ai/v1/visits/latest

{
  "request_id": "0c284695-cf0b-4755-8beb-0a2e9536595e",
  "visitor_id": "aa8c616a-8a25-4a5e-bee2-a9a08e5128a4",
  "device_id": "6a45967d-1371-9652-ba99-b01ea3992208",
  "user_hid": "u_9f2a41c7",
  "public_ip": { "ip": "62.197.149.124", "country": "United States" },
  "local_ip": { "ip": "45.83.91.7", "country": "United States" },
  "connection_type": "vpn",
  "os": "Windows",
  "browser": "Chrome",
  "device_type": "desktop",
  "risk_score": 85,
  "signals": [
    { "name": "antidetect_browser", "weight": 60 },
    { "name": "vpn", "weight": 15 },
    { "name": "timezone_mismatch", "weight": 10 }
  ]
}

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