Synthetic Identity: Detection & Prevention

Key Takeaway 1 Synthetic identity fraud relies on creating entirely new identities using a combination of real and fabricated Personal Identifiable Information (PII).

Key Takeaway 2 Detecting synthetic identities requires a multi-layered approach beyond traditional identity verification, leveraging behavioral analytics and data correlation.

Key Takeaway 3 Proactive fraud detection APIs can significantly reduce losses associated with synthetic identity fraud by identifying and flagging suspicious applications early in the process.

Key Takeaway 4 As AI advances, so too will synthetic identity creation; continuous adaptation of detection methods is vital.

Understanding Synthetic Identity Fraud

Synthetic identity fraud is a rapidly growing type of fraud that involves using a combination of real and fabricated PII to create a completely new identity. Unlike traditional identity theft, which involves taking over an existing identity, synthetic identity fraud creates a 'phantom' identity that never existed before. This is often done by combining a real name with a fake Social Security number (SSN), date of birth, and address. The fraudsters then build a credit history for this synthetic identity, often applying for and obtaining loans, credit cards, and other forms of credit. The Financial Crimes Enforcement Network (FinCEN) estimates that synthetic identity fraud resulted in losses of $6 billion in 2016 alone, and this number continues to climb, reaching an estimated $20 billion in 2023. This fraud is particularly challenging to detect because the synthetic identity doesn't have an existing fraud history to flag it. Traditional identity verification methods, relying on matching against existing databases, are often ineffective. The sophistication of these schemes is increasing, with fraudsters using increasingly realistic fabricated data and employing techniques to avoid detection.

How Synthetic Identities are Created

The creation of a synthetic identity typically follows a pattern. Fraudsters often start by obtaining a real name and a Credit Reporting Agency (CRA) file. This can be achieved through data breaches, phishing scams, or even purchasing PII on the dark web. They then generate a fake SSN, often using established patterns to ensure it appears valid. This fake SSN is then combined with the real name and a fabricated address. Once the synthetic identity is created, the fraudster begins building a credit profile. This involves opening small accounts, such as secured credit cards or retail store credit lines, and making timely payments to establish a positive credit history. Once the credit profile is established, the fraudster can then apply for larger loans and credit lines, often maximizing the credit limits and then defaulting on the debt.

The Role of APIs in Detection

Detecting synthetic identities requires a more sophisticated approach than traditional identity verification. This is where advanced APIs come into play. APIs that offer data enrichment and correlation can help identify anomalies and inconsistencies that might indicate a synthetic identity. Specifically, APIs can perform the following checks:

• Cross-device analysis: Identifying if an application originated from a suspicious device or network.
• Behavioral biometrics: Analyzing typing speed, mouse movements, and other behavioral patterns to detect anomalies.
• Data point correlation: Checking for inconsistencies between different data points, such as the address and phone number.
• Velocity checks: Identifying applications that are submitted in rapid succession or from multiple locations.
• Social Security Number (SSN) validation: Using specialized APIs to verify the validity of the SSN, including checks against death records and other databases.

Didit's identity platform offers a suite of APIs designed to mitigate synthetic identity fraud, including advanced data enrichment and behavioral analysis tools, all accessible via a single integration.

Advanced Fraud Detection Techniques

Beyond API integration, more advanced fraud detection techniques are crucial. Machine learning (ML) models can be trained to identify patterns and anomalies indicative of synthetic identities. These models can analyze vast amounts of data, including application data, credit bureau data, and fraud reports, to identify high-risk applications. Another technique is network analysis. This involves mapping the relationships between different entities, such as addresses, phone numbers, and SSNs, to identify suspicious connections. For example, if multiple applications are linked to the same fake address or SSN, it could be a sign of synthetic identity fraud. Furthermore, utilizing device fingerprinting and IP address geolocation can provide valuable insights. Discrepancies between the applicant’s stated location and their IP address, or the use of a virtual private network (VPN), can raise red flags. The more data points that are analyzed, the more accurate the fraud detection will be.

How Didit Helps

Didit provides a comprehensive solution for combating synthetic identity fraud. Our platform combines multiple layers of security, including:

• Document Verification: Robust ID document verification with tamper detection and data extraction.
• Biometric Authentication: Liveness detection and face match to ensure the applicant is a real person.
• AML Screening: Screening against global watchlists to identify potential fraudsters.
• Fraud Signals: Analysis of IP address, device data, and behavioral signals to detect suspicious activity.
• Workflow Orchestration: Customizable workflows to adapt to changing fraud trends.

Didit's API-first approach allows for seamless integration with existing fraud prevention systems, providing a flexible and scalable solution for businesses of all sizes.

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