Stop SaaS fraud before
it drains your revenue

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 types 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 detects Multi-accounting, one person behind many accounts, using a persistent identifier that works across sessions regardless of email, IP, or cleared cookies.

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.

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; risk signals such as VPN, proxy, Tor, anti-detect browser, and IP reputation; High-Risk Events in the analytics dashboard (Multi-accounting, Account sharing, Impossible travel, and Account takeover), each with Medium or High confidence. When a new signup matches an identity already linked to a prior account, ShieldLabs detects Multi-accounting directly. ShieldLabs delivers the risk score, signals, and events via API and Webhooks, so you can hold, limit, or downgrade the account before the trial is activated.

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.

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.

Yes, ShieldLabs detects 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 detects Account sharing as a High-Risk Event with Medium or High confidence, so a SaaS team can prompt an upgrade, split the seat, or apply its seat policy.

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 the risk score, risk signals, and High-Risk Events in the analytics dashboard and via 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 rolling out to production.

ShieldLabs detects risky signups out of the box and helps block fraudulent and abusive traffic. Every signup gets a 0-100 risk score in one of three bands, plus any High-Risk Events, and you choose the action for each case: Dangerous (60-100): hold the signup for review or route it to a restricted trial; Suspicious (30-59): trigger email or phone verification before the trial is provisioned; Trusted (0-29): let the signup through with no extra friction. ShieldLabs includes 5,000 identifications on the free tier so you can see results on real traffic before rolling out to production.