Synthetic identity fraud is quickly becoming one of the most complex forms of identity theft, posing a serious challenge to businesses, particularly those in the banking and finance sectors. Unlike traditional identity theft, where an entire identity is stolen, synthetic identity fraud involves combining real and fake information to create a new identity. Fraudsters often use real details such as Social Security Numbers (SSNs), especially those belonging to children or the elderly, which are less likely to be monitored. This blend of authentic and fabricated data makes it difficult for organisations to detect the fraud early, leading to financial losses.
What Is Synthetic Identity Fraud?
At its core, synthetic identity fraud is the creation of a fake identity using both real and made-up information. Criminals often use a legitimate SSN paired with a fake name, address, and date of birth to construct an identity that doesn’t belong to any actual person. Once this new identity is formed, fraudsters use it to apply for credit or loans, gradually building a credible financial profile. Over time, they increase their credit limit or take out large loans before disappearing, leaving businesses to shoulder the debt. This type of fraud is difficult to detect because there is no direct victim monitoring or reporting the crime.
How Does Synthetic Identity Fraud Work?
The process of synthetic identity fraud typically begins with criminals obtaining real SSNs, often through data breaches or the dark web. Fraudsters then combine this information with fake personal details to create a new identity. Although their first attempts at opening credit accounts may be rejected, these applications help establish a credit file for the fake identity. Over time, the fraudster builds credit by making small purchases and timely payments to gain trust. Eventually, they max out their credit lines and disappear, causing major financial damage to lenders and businesses.
Comparing Traditional VS Synthetic Identity Theft
The primary distinction between traditional and synthetic identity theft lies in how the identity is used. Traditional identity theft involves using someone’s complete identity to make unauthorised purchases or take out loans. Victims usually notice this quickly and report it, helping prevent further fraud. In contrast, synthetic identity theft is harder to detect because the identity is partly or entirely fabricated, and no real person is actively monitoring it. This gives fraudsters more time to cause substantial financial damage before the fraud is identified.
The Financial Impact of Synthetic Identity Theft
Synthetic identity fraud is costly. According to the Federal Reserve, businesses lose an average of $15,000 per case, and losses from this type of fraud are projected to reach $23 billion by 2030. Beyond direct financial losses, businesses also face operational costs related to investigating fraud, potential reputational damage, and legal or regulatory consequences if they fail to prevent such incidents. These widespread effects calls for stronger security measures.
How Can Synthetic Identity Fraud Be Detected?
While synthetic identity fraud is complex, there are several ways businesses can identify potential fraud. Monitoring for unusual account behaviours, such as perfect payment histories followed by large transactions or sudden credit line increases, is essential. Document verification processes, along with cross-checking identity details such as SSNs, can also help catch inconsistencies. Implementing biometric verification and using advanced analytics and AI-driven tools can further improve fraud detection. Collaborating with credit bureaus and educating employees and customers about potential fraud risks are other important steps companies can take to safeguard their operations.
Preventing Synthetic Identity Theft
Preventing synthetic identity theft requires a multi-layered approach. First, businesses should implement strong data security practices like encrypting sensitive information (e.g., Social Security Numbers) and using tokenization or anonymization to protect customer data.
Identity verification processes must be enhanced with multi-factor authentication (MFA) and Know Your Customer (KYC) protocols, including biometrics such as facial recognition. This ensures only legitimate customers gain access.
Monitoring customer behaviour through machine learning and behavioural analytics is key. Real-time alerts for suspicious activity, such as sudden credit line increases, can help detect fraud early.
Businesses should also adopt data minimisation— collecting only necessary data—and enforce data retention policies to securely delete outdated information. Additionally, regular employee training on data security, phishing, and fraud prevention is crucial for minimising human error.
Conducting security audits and assessments helps detect vulnerabilities, ensuring compliance with data protection laws like GDPR or CCPA. Furthermore, guarding against insider threats through background checks and separation of duties adds an extra layer of protection.
When working with third-party vendors businesses should vet them carefully to ensure they meet stringent security standards, and include strict security measures in contracts.
Lastly, a strong incident response plan should be in place to quickly address breaches, investigate fraud, and comply with legal reporting requirements.
Synthetic identity fraud poses a serious challenge to businesses and industries, particularly those reliant on accurate identity verification. As criminals become more sophisticated, companies must adopt advanced security measures, including AI-driven fraud detection tools and stronger identity verification protocols, to stay ahead of the evolving threat. By doing so, they can mitigate financial losses and protect both their business and customers from this increasingly prevalent form of fraud.
