A Pattern is a pre-built correlation that links related accounts, devices, and anonymous signals to surface activity indicating abuse, such as many accounts on one device or one account active from two countries at once. Runs continuously across your full traffic history, not just a single visit; Correlates related entities (accounts, devices, identifiers) into one connected view; Surfaces in the dashboard with a severity rating, Suspicious or Dangerous, and the accounts involved. ShieldLabs ships these patterns ready to use, so you see activity indicating multi-accounting, account takeover, and ban evasion without writing custom rules.

What is the difference between a Risk Score and a Pattern?

A Risk Score rates a single visit; a Pattern correlates many visits, accounts, and devices over time. They answer different questions and work together. Risk Score: a 0-100 rating built from the signals collected on one visit, returned on the first visit; Pattern: a cross-session correlation that connects related accounts and devices across your full history; Together: the score flags a risky visit in the moment, the pattern reveals the wider ring it belongs to. ShieldLabs surfaces a single Risk Score and a connected Pattern side by side in the same analytics dashboard.

Does ShieldLabs detect credential stuffing?

Yes. Credential stuffing produces a distinct, correlatable signature, many devices reaching one account in a short window from new locations, which ShieldLabs surfaces as a pre-built pattern indicating account compromise. Looks for the new-device-and-new-country-on-one-account combination across sessions; Correlates the devices and egress IPs hitting a single persistent identifier; Surfaces the result under Possible Abuse Cases with a severity rating and the linked account. ShieldLabs surfaces this pattern automatically, so signals indicating stolen credentials reach your team without any custom rules to write.

How quickly do patterns appear after a suspicious event?

Patterns appear in real time: the moment correlated activity crosses the threshold that defines the pattern, it surfaces in the dashboard. The first Risk Score is available on the first visit, within roughly 5 minutes of integration. A new visit is scored on arrival, on the first visit; Cross-session correlations update continuously as accounts and devices connect; A crossed threshold surfaces the pattern with its severity rating and linked accounts immediately. ShieldLabs surfaces these patterns in the analytics dashboard the moment they form, with up to 99% identification accuracy behind them.

Patterns

Surface Abuse. Stop Fraud

Detect multi-accounting, account takeover fraud, credential stuffing, ban evasion, and every major abuse vector the moment they hit your traffic

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ShieldLabs Patterns hero showing one user account with three abuse patterns branching outward

More revenue from real customers

Block abuse rings before they flood the funnel with accounts that will never pay

Less money lost to abuse and fraud

Catch multi-accounting and promo abuse before they drain margins

Acquisition budget spent on real users

Know which signups are fraudulent before spend is wasted

Growth metrics worth acting on

Separate real signups from fake ones so CAC, LTV, and conversion data reflects reality

Detect abuse with in-depth pattern analytics

No fraud model to build

Surface the most common abuse vectors with pre-built patterns, active from day one

Hidden connections, made visible

Connect accounts, devices, and anonymous signals into one persistent identity graph

Identify risky users instantly

Score every visit and separate normal users from suspicious and dangerous ones

All active patterns, one view

Track active patterns, affected accounts, and severity across the full traffic history over time

Patterns · last 7 days · 5 use cases · 3,152 hits

Multi-accounting

Many accounts on one device

1,284DANGEROUS

Account takeover

Many devices on one account

842DANGEROUS

Impossible travel

Multiple countries on one account

517SUSPICIOUS

Account sharing

Changing IDs on one account

298DANGEROUS

Account farms

Many accounts on one IP

211SUSPICIOUS

normal ·12,847suspicious ·728dangerous ·2,424

From first visit to detected pattern

Persistent identity

Identify users across sessions, even with cleared cookies and rotated IPs

Signal collection

Collect 100+ signals across device, browser, OS, IP, and network layers, identifying anonymous signals

Abuse detection

Combine user accounts, visitors, and anonymous signals with risk scoring to detect abuse over time

Results in the analytics dashboard

Surface possible abuse cases with suspicious and dangerous breakdowns and full data per pattern

Stop abuse in any vertical.

SaaS

Stop trial abuse, freemium exploitation, and plan-limit bypass from accounts that will never pay

iGaming

Eliminate bonus abuse, affiliate fraud, and promo multi-accounting before payouts reach fake accounts

E-commerce

Prevent repeat use of sign-up bonuses, vouchers, and first-time-buyer offers across multiple accounts

Fintech

Stop financial account takeover, scammers exploiting signup bonuses, bypassing KYC through multiple accounts, and abusing referral programs

Cryptocurrency and Web3

Defend airdrops and reward campaigns from sybil attacks and coordinated farming rings

Media and Subscriptions

Detect password sharing and credential reselling before revenue bleeds to free riders

See the abuse patterns in your traffic.

Free 5,000 one-time identifications, with transparent pricing that scales with your needs

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