Stop Abuse Across Accounts

Protect Your Platform from Multi-Account Abuse

Multi-accounting is a common abuse pattern where users create multiple accounts to bypass restrictions, claim multiple rewards, or manipulate your platform. Our advanced detection identifies linked accounts and prevents abuse. This type of abuse can take many forms: users creating multiple accounts to claim welcome bonuses multiple times, bypassing account limits or restrictions, manipulating voting or rating systems, or evading bans. The financial and operational impact can be significant, affecting your revenue, user experience, and platform integrity. Our comprehensive detection system uses multiple signals to identify when accounts are linked, even when users attempt to hide their connections.

Key Features

Account Linking Detection

Identify accounts that share devices, IP addresses, payment methods, or behavioral patterns to detect multi-account abuse. Our system creates a comprehensive graph of account relationships by analyzing shared characteristics. Even sophisticated abusers who use VPNs and different devices leave traces that our algorithms can detect, such as similar typing patterns, browsing behaviors, or account setup sequences.

Real-Time Prevention

Block suspicious account creation attempts and flag existing accounts that show signs of being part of a multi-account network. As soon as a new account is created, our system checks it against existing accounts for potential links. If connections are detected, the account can be automatically flagged or blocked based on your policies, preventing abuse before it starts.

Behavioral Analysis

Analyze user behavior patterns across accounts to identify coordinated abuse and prevent fraudulent activities. Users who control multiple accounts often exhibit similar behaviors across those accounts, such as similar navigation patterns, feature usage, or interaction timing. Our behavioral analysis engine identifies these patterns and connects accounts that show suspicious similarities.

Automated Enforcement

Automatically enforce policies based on risk scores, from warnings to account suspension, protecting your platform without manual intervention. Configure automated responses that match your business needs. Low-risk cases might receive warnings, while high-confidence multi-account abuse can result in immediate account suspension or merging of linked accounts.

Detection Signals We Analyze

Device & Browser Signals

1. • Hardware fingerprints and device characteristics
   1. • Browser configurations and extensions
   2. • Screen resolution and display settings
   3. • Installed fonts and system preferences

Network & Location Signals

1. • IP address patterns and subnet analysis
   1. • Geographic location and timezone data
   2. • Network provider and connection type
   3. • VPN and proxy detection

Payment & Identity Signals

1. • Shared payment methods and billing addresses
   1. • Email domain patterns and providers
   2. • Phone number patterns and carriers
   3. • Identity document verification results

Behavioral Signals

1. • Typing patterns and input timing
   1. • Mouse movements and click patterns
   2. • Navigation and feature usage patterns
   3. • Account setup and onboarding sequences

Common Multi-Account Abuse Scenarios

Promotional Abuse

Users create multiple accounts to claim welcome bonuses, referral rewards, or limited-time promotions multiple times. This directly impacts your marketing budget and can skew your customer acquisition metrics.

Limit Evasion

Users bypass account limits, restrictions, or usage quotas by creating additional accounts. This can affect resource allocation, fair usage policies, and platform economics.

Rating Manipulation

Multiple accounts are used to artificially inflate or deflate ratings, reviews, or votes, undermining trust in your platform's content and recommendations.

Ban Evasion

Users who have been banned create new accounts to continue their abusive behavior, circumventing your moderation efforts and potentially harming other users.