Link Analysis

What is Link Analysis?

Link analysis is a data-driven approach used to identify and visualize relationships between different entities within a system. It maps connections between accounts, devices, locations, transactions, or other data points to detect unusual patterns, uncover hidden networks, and identify fraudulent schemes. Fraudsters often operate in groups or use complex, interconnected systems to mask their activities, making link analysis an essential tool for exposing these hidden relationships.

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This technique is commonly employed in industries like banking, e-commerce, and telecommunications to combat fraud, such as money laundering, synthetic identity fraud, and organized promo abuse.

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How Does Link Analysis Work?

Data Aggregation

• Link analysis collects data from various sources, such as user accounts, transaction histories, device fingerprints, and IP addresses.

Connection Mapping

The system creates a network of relationships by linking entities based on shared characteristics or activities. For example:

• Multiple accounts using the same email address, phone number, or IP address.

• Transactions from different accounts flowing to the same bank account or drop address.

Pattern Identification

By analyzing the network, the system identifies unusual or suspicious patterns, such as:

• High-velocity transactions between connected accounts.

• Clusters of accounts with shared attributes exhibiting similar behavior.

• Circular fund transfers or device-sharing patterns indicative of organized fraud.

Risk Scoring and Alerts

• Entities with strong connections to known fraudsters or high-risk activities are flagged for review, helping investigators focus their efforts.

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Use Cases

Legitimate Scenarios

• Banking: Detecting money laundering networks by tracing the flow of funds between accounts.

• E-Commerce: Identifying fraud rings exploiting promo campaigns by linking multiple suspicious accounts.

• Telecommunications: Tracing connections between devices and accounts to detect SIM swap fraud or account takeovers.

Fraudulent Use Cases

• Synthetic Identity Fraud: Fraudsters use multiple fake identities with overlapping data points to commit financial fraud.

• Money Laundering: Complex networks of transactions obscure the origin of illicit funds, which link analysis can expose.

• Promo and Referral Abuse: Fraud rings create interconnected accounts to exploit promotions or referral programs repeatedly.

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Impacts on Businesses

Positive Impacts

• Enhanced Fraud Detection: Link analysis helps uncover complex fraud schemes that may go unnoticed with traditional rule-based systems.

• Proactive Fraud Prevention: Detecting connected fraudulent entities in real-time reduces the impact of large-scale fraud.

• Improved Investigations: Visualizing relationships and patterns makes it easier for analysts to investigate and resolve fraud cases.

Negative Impacts

• Resource Intensiveness: Implementing link analysis systems requires significant investment in technology, data integration, and skilled personnel.

• False Positives: Overly strict rules or poor data quality can lead to legitimate accounts being flagged incorrectly, affecting customer experience.

• Fraudster Adaptation: Sophisticated fraudsters may deliberately create false connections or camouflage their activities to evade detection.