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.
Orlando Family physicians, which has 10 clinics in central Florida, has agreed to pay affected patients who submit a claim by July 1 a reimbursement and provide them two years of free credit monitoring. Patients may earn up to $225 or, for those whose SSNs were stolen, up to $7,500 depending on what kind of private information the thieves obtained.
However, as part of the compensation, the physician organization denies any responsibility for the data heist.
Court records reveal that the crime took place in April 2021 after thieves used a phishing scam to access the email accounts of four employees. As per Orlando Family Physicians, it “immediately” took the necessary steps, containing the intrusion and hires a “leading” security shop to determine the scope of intrusion.
The health group, a few months later, published a notice on its website and sent letter to victims whose private information was compromised. The data apparently includes names, demographic information, health information, including diagnosis, medical record numbers, patient account numbers, passport numbers, providers and prescriptions; health insurance details, including legacy Medicare beneficiary numbers generated from the person's Social Security number or other subscriber identification number.
However, according to the physician group “, the available forensic evidence indicates that the unauthorized person’s purpose was to commit financial fraud against OFP and not to obtain personal information about the affected individuals.”
Moreover, OFP reported to the US Department of Health and Human Services, saying it potentially affected 447,426 individuals.
After the attorneys take their cut, of course, those hundreds of thousands of people whose personal information most certainly ended up for sale on a hacking forum are now eligible for a compensation. The settlement's overall sum is still undisclosed.
There are two groups within the class that stand to gain monetarily. The first group, individuals who incurred out-of-pocket costs as a result of the theft, may file a claim for up to $225 in duly substantiated costs. This covers any expenses incurred while freezing or unfreezing credit reports, paying for credit monitoring services, or contacting banks about the occurrence, including notary, fax, mailing, copying, mileage, and long-distance phone costs.
The victims can also file a claim for a time limit of up to three hours, compromised due to the security breach at the rate of $25 per hour.
The second category consists of victims whose Social Security numbers were taken. These people are eligible to file claims for up to $7,500 for confirmed instances of identity theft, fabricated tax returns, or other forms of fraud that can be linked back to the initial hack. They as well can claim up to eight hours of lost time at $25 per hour.
The settlement comes as ransomware gangs and other cybercriminals intensify their attacks on hospitals and other healthcare organizations, and the lawyers have responded by bringing numerous class-action cases.
The aforementioned class-action lawsuit is proposed following an intrusion in February, wherein the BlackCat malware infiltrated one of the Lehigh Valley Health Network physician’s networks, stole sensitive health records belonging to more than 75,000 people, including pictures of patients receiving radiation oncology treatment, and then demanded a ransom to decrypt the files and stop it from posting the records online.