Understanding Synthetic Identity Fraud and Its Impact

Learn what synthetic identity fraud is, how it works, its impact on businesses and consumers, and effective strategies to prevent this growing cyber threat.

Aiswarya N Aiswarya N
 Aug 6, 2025
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Understanding Synthetic Identity Fraud and Its Impact
Understanding Synthetic Identity Fraud and Its Impact

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Synthetic identity fraud is rapidly becoming one of the fastest-growing threats in digital security. Fraudsters combine real and falsified personal data to create new, fake identities that exploit systems over time. In 2024 alone, synthetic identity fraud losses in the U.S. reached approximately $3.3 billion, pushing financial industries to rethink fraud prevention strategies 

Here, you’ll explore what synthetic identity fraud in cyber security means, how it works, different types, its wide-ranging impact, and actionable prevention strategies.

What is Synthetic Identity Fraud in Cyber Security

Synthetic identity fraud in cyber security involves assembling a new identity by mixing a real Social Security number or official identifier with fabricated data such as a fake name, birthdate, or address. Unlike traditional identity theft, this method doesn't directly steal an existing person’s identity. Instead, it builds a fictional persona that can bypass detection while credit is established over time 

Once the synthetic persona is established, fraudsters may open bank accounts, apply for loans, or use services until they "bust out" maxing out lines of credit and disappearing.

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How Synthetic Identity Fraud Works

The lifecycle of synthetic identity fraud is often slow and deliberate, making it incredibly difficult to detect early. Fraudsters craft these fake personas carefully, nurturing them until they are deemed “creditworthy” by financial institutions. Here's how the process typically unfolds:

Stealing a Real SSN or Identifier

Fraudsters often begin by acquiring a legitimate Social Security Number (SSN), typically from children, the elderly, or inactive users whose credit activity is minimal. These details are usually harvested from large-scale data breaches or bought on the dark web.

Fabricating Additional Identity Details

They pair the stolen SSN with fabricated information like a fake name, birthdate, phone number, or address. This combination forms a new "synthetic" identity that appears unique and unrelated to the actual SSN holder.

Building Creditworthiness Over Time

Once created, the synthetic identity is used to apply for credit cards or small loans. While initial applications may be denied, these attempts still create a credit file. Fraudsters then gradually build credit scores by becoming authorized users on real accounts or getting small lines of credit approved. This phase can last months or even years, adding to the illusion of legitimacy.

Exploiting Financial Trust

After building a solid credit profile, fraudsters start applying for larger loans, high-limit credit cards, or auto financing. Institutions, trusting the now-aged identity, extend significant financial resources, setting the stage for the final phase.

“Bust Out” and Abandonment

The fraudster maxes out credit limits and vanishes, leaving unpaid debts behind. Because the identity is synthetic (not tied to a real person), debt recovery is nearly impossible.

These personas are particularly dangerous because they don’t match any known individual. That means they often pass undetected through traditional fraud filters and identity checks. In fact, many systems don’t flag them until charge-offs and non-payments trigger deeper investigations by which time, the financial damage is already done.

Types of Synthetic Identity Fraud

Synthetic identity fraud in cyber security generally falls into two main categories based on how the identity is constructed: manipulated and manufactured. Understanding these distinctions is crucial for developing effective fraud detection techniques and identity fraud prevention strategies.

1. Manipulated Synthetic Fraud

This type involves using mostly legitimate identity information such as a real name, date of birth, or Social Security Number (SSN) with one or two altered details. For example:

  • Changing the address slightly
  • Modifying the birthdate
  • Altering a few characters in the name

These small changes make the identity seem authentic while evading automated verification systems. Manipulated fraud is often used to bypass credit score restrictions or reset tarnished credit histories, and it can take longer to detect since the credentials closely resemble real profiles.

2. Manufactured Synthetic Fraud

This form involves entirely fabricated identities, except for one critical piece of real information, typically a stolen SSN, often from children, the elderly, or deceased individuals. The fraudster then fabricates:

  • A fake name
  • A made-up date of birth
  • A constructed address
  • False employment or income details

Because it combines real and fake information, this type of fraud is sometimes called creating a "Frankenstein identity." Manufactured synthetic identities are particularly dangerous because:

  • They often pass initial credit checks
  • They can be maintained and aged over time to increase credibility
  • They result in larger financial losses when loans or credit cards default

Impact of Synthetic Identity Fraud

The impact of synthetic identity fraud in cyber security is far-reaching, affecting financial institutions, individuals, and regulatory environments worldwide. Below are key statistics and consequences that highlight the growing threat:

  • Synthetic identity fraud accounts for nearly 80% of all new account fraud, according to the U.S. Federal Reserve.
  • TransUnion reported $3.3 billion in fraud exposure for U.S. financial institutions due to synthetic identities by the end of 2024, a 3% increase from 2023.
  • In the UK, Experian observed a 60% increase in synthetic identity cases in 2024, with 29% of identity fraud incidents linked to synthetic identities.
  • The average charge-off per synthetic fraud incident is approximately $15,000, making it one of the most financially damaging types of identity fraud.
  • Over 50% of financial organizations report their defenses against synthetic identity fraud are only “somewhat effective”, according to a 2023 Wakefield Research survey.
  • Emerging markets in Asia and Latin America are also witnessing a rise in synthetic identity fraud, especially with increased digital banking adoption.
  • Children and underserved populations are frequent victims, as their identities often go unused for long periods and are easy targets for fraudulent use.
  • Businesses face regulatory scrutiny and reputational damage due to increased global emphasis on compliance with digital identity and anti-fraud regulations.
  • Synthetic identity theft contributes to higher operational costs, as institutions invest more in fraud detection, customer remediation, and legal processes.
  • The growing sophistication of fraudsters makes synthetic identities harder to detect with traditional tools, increasing the need for advanced fraud detection techniques and AI-based monitoring systems.

How to Prevent Synthetic Identity Fraud

Combatting synthetic fraud in cyber security requires a multilayered strategy:

  • Deploy advanced fraud detection techniques like machine learning and behavioral analytics.
  • Use identity verification tools that cross-validate SSNs, addresses, and name consistency in real time.
  • Implement synthetic fraud detection models that flag anomalies in credit inquiries or opening behaviors.
  • Share identity anomaly data across institutions to identify patterns before financial damage accrues.
  • Conduct regular audits and validation of customer onboarding processes to catch fabricated identities early.

These strategies help institutions proactively detect synthetic identity fraud before significant loss occurs.

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In short, synthetic identity fraud in cyber security is a stealthy, high-impact form of financial crime that takes advantage of system blind spots. It’s poised to cost the U.S. alone over $23 billion by 2030, as identities grow more sophisticated and detection methods lag behind.

Understanding how synthetic identity theft differs from traditional fraud, recognizing common types and fraud processes, and adopting strong identity verification controls are critical steps in safeguarding institutions and individuals alike. As this form of cybercrime evolves, staying informed and vigilant is essential.

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