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Accounts linked graph showing connections between related accounts

Multi-accounting (also known as duplicate accounts) occurs when a single person creates and operates multiple accounts on your platform. This is a common fraud pattern that can lead to significant losses through promotional abuse, collusion, and other fraudulent activities.

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Why Multi-Accounting Is a Problem

Promotional Abuse

Claiming welcome bonuses, referral rewards, or discounts multiple times

Collusion

Unfair advantages in gaming, gambling, or marketplace platforms

Fake Engagement

Creating artificial engagement or fake reviews

Resource Abuse

Consuming limited resources or free trials repeatedly

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Components of Verisoul’s Multi-Accounting Detection

Verisoul’s multi-accounting detection system consists of several interconnected components that work together to provide an accurate risk assessment.
1

Match Types

Verisoul identifies the following match types between accounts:

Browser Match

The only deterministic match type with zero false positives

Always results in a score of 1 (highest confidence)

Based on browser-specific identifiers

Device Match

Based on hardware and software characteristics

Includes device fingerprinting techniques

Probabilistic with varying confidence levels

Network Match

Analysis of network patterns and characteristics

Includes IP address analysis and packet inspection

Probabilistic with varying confidence levels

Email Match

Analyzes string similarity of normalized email addresses

Strips special characters like dots (.) and plus (+) signs

Identifies users creating variations of the same email

Phone Match

Exact match on the normalized E.164 phone number passed in the account object

Full-strength match, like browser and email

Requires the phone field on authenticate

Username Match

Exact match on the normalized username passed in the account object

Full-strength match — catches obvious handle reuse across accounts

Requires the username field on authenticate

Name Match

Exact match on the normalized name (lowercased, punctuation and diacritics stripped), built from the name parts provided

Moderate-strength match — a name match alone never produces a full-strength link, but name plus another match (device, network, email) does

Requires first_name and/or last_name on authenticate

2

Account-to-Account Scores

Each potential connection between two accounts receives a score (0-1) based on several factors:
Match type strength: Browser, email, phone, and username matches (full strength) > Name matches (moderate strength) > Device matches > Network matches
Number of sessions matched: More matching sessions increase confidence
Fingerprint uniqueness:
  • Globally unique fingerprints provide higher confidence
  • Common fingerprints (seen frequently across many users) provide lower confidence
Time between sessions:
  • Sessions close together in time provide stronger signals
  • Sessions weeks apart provide weaker signals
Two accounts sharing a unique device fingerprint across multiple sessions within minutes would receive a high account-to-account score, while two accounts sharing only a common network characteristic weeks apart would receive a low score.
3

Multi-Accounting Probability

The multiple_accounts field is the culmination of all account-to-account scores, representing the overall likelihood that an account is part of a multi-accounting scheme:
Range: 0 (no risk) to 1 (highest risk)
Primary risk indicator: This is the most important field for assessing multi-accounting risk
This probability is calculated by:
  1. Evaluating all account-to-account scores
  2. Considering the number of linked accounts
  3. Applying the accounts linked threshold configuration

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Accounts Linked Threshold

The accounts linked threshold is a configurable parameter that affects how the multiple_accounts field is calculated:
Default value: 1 (a single perfect match results in probability of 1)
Custom values: Can be adjusted based on your business needs
Example: Setting to 3 allows users to have up to 3 accounts before triggering high probability scores
Purpose: Helps reduce false positives while still preventing scaled fraud
Contact the Verisoul team to configure the accounts_linked_threshold for your specific use case.

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Accounts Linked API Response

When you query the Verisoul API for accounts linked to a specific account, you’ll receive data about potential multi-accounting:

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How Scores Roll Up Into Multi-Accounting Probability

The relationship between account-to-account scores and multiple accounts score:
1. Strong matches dominate: A single strong match (especially a browser match with score 1) can result in a high probability
2. Quantity vs. quality: Many weak matches may not significantly increase probability
3. Threshold application: The accounts_linked_threshold determines how many strong matches are needed for a high probability

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Risk Examples

High Risk

An account with one perfect browser match (score 1) would likely have a high multiple_accounts score (near 1)

Medium Risk

An account with several medium-strength device matches (scores 0.4-0.6) might have a moderate multiple_accounts score

Low Risk

An account with many low-strength network matches (scores 0.1-0.3) would likely have a low multiple_accounts score

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Why Fingerprints Aren’t Returned

1. Fraud prevention effectiveness: Raw fingerprints are easily changed by fraudsters who modify device attributes
2. Stability issues: Fingerprints aren’t exact, can have false positives, and aren’t always stable
3. Complexity: Resolving and actioning a graph database is complex
4. Automation: Verisoul automates this process while remaining flexible enough to tune to your ecosystem

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Viewing Account Linkages

The graph of linked accounts is viewable in the Verisoul dashboard and can be retrieved at any time via the accounts linked API. This provides a visual representation of potential multi-accounting clusters.
Accounts linked graph showing connections between related accounts