Signup Fraud
Fake signups aren’t just noise; they’re the entry point for abuse, spam, and downstream fraud. Worst of all, they’re getting harder to catch. Fraudsters use bots, scripts, and synthetic identities to create accounts that pass basic checks and blend in with real users. Elephant helps you catch these fake accounts at signup before the damage spreads across your platform.
Once they’re in, fake accounts start to act like real users. Some stay quiet. Some launch attacks. Many slip through unnoticed until the damage is done. From promo abuse to coordinated credential farms, signup fraud doesn’t look like a breach. It looks like growth, unless you know what to look for.
A burst of new accounts, each tied to a unique IP and real-looking email. On the surface, nothing looks wrong. Underneath, a credential farm operating at scale.
Accounts that clear verification checks, then trigger abuse, spamming users, posting scams, or scraping content. They look real just long enough to cause damage.
Fake signups that appear active to farm referral bonuses, free trials, or promo codes. They inflate growth metrics while draining revenue and distorting performance.
Accounts created in bulk and left to age, often sold or activated later for scams, takeovers, or synthetic identity fraud. Harmless today but dangerous tomorrow.
The strongest decisions rely on more than just device or IP data. Layered signals, with velocity, volatility, and behavioral context, surface risk patterns that other tools struggle to connect.
Noisy signals, brittle integrations, and privacy pressure have made identity decisions harder than they should be. We’ve created two flexible solutions that are fast to integrate, privacy-native by design, and built to fit your workflow, not force a new one.
Plug real-time trust scores and signal intelligence directly into your decision logic, so you can automate approvals, reduce fraud, and skip the rework.
A visual tool for fraud analysts to spot risk fast. Surface identity signals and connections at a glance, ideal for confident manual reviews and edge-case decisions.
