What is Synthetic Identity Fraud? How to Detect & Prevent it

Last Updated: June 21, 2024 by Bence Jendruszak

Criminals have developed sophisticated methods for creating convincing identities, including synthetic identity fraud, which is exacerbated by the theft of approximately 14 million identities annually. These fraudulent identities combine stolen, fake, and artificially generated data, enabling criminals to carry out a wide range of illicit activities and construct entirely new personas by merging these elements. The complexity of synthetic identity fraud presents significant challenges in terms of detection and prevention.

Highlights:

Synthetic identity fraud fraud involves blending real and fake details to create fictitious identities for financial crimes.
Synthetic identity fraud is difficult to spot, but using digital footprint analysis, IP and BIN lookups, device fingerprinting, and behavior analysis can aid detection. Machine learning can enhance this by uncovering hidden patterns.
To combat synthetic ID fraud, companies need an advanced fraud detection platform rather than relying on static checks.

What Is Synthetic Identity Fraud?

Synthetic identity fraud is a type of fraud that involves creating a fictitious identity by combining fake and real personal information. Criminals use these identities to defraud businesses, bypass KYC checks, exploit promotions, obtain credit, make purchases with stolen cards, and launder money. This type of fraud is difficult to detect because it blends authentic and fake data, making the synthetic identity seem legitimate, posing a significant and growing threat to the financial sector and other industries reliant on accurate identity verification.

Fraudsters create synthetic identities by altering genuine ID data, such as changing birth dates or social security numbers, combining real personal information from various sources, or fabricating entirely new identity elements, like randomized social security numbers. Regardless of their makeup, it is crucial for businesses to quickly identify synthetic identities. Early detection is essential because these fraudulent identities often lead to more complex fraud schemes.

How Are Synthetic IDs Created?

Synthetic ID creation typically works this way:

Identity Creation: The fraudster blends stolen, manipulated, or entirely fabricated data to create a synthetic identity. For instance, they might use a real but inactive social security number stolen from a child.
Credit Application: Initially, the fraudster applies for credit to establish a credit history for the synthetic identity. This often requires multiple attempts as lenders are cautious with new, unproven identities.
Building Credit: Once approved for a small line of credit, the fraudster meticulously builds a positive credit history by making regular payments. This improves the synthetic identity’s credit score over time.
Exploiting Credit: With an enhanced credit score, the fraudster applies for larger lines of credit, eventually withdrawing funds and disappearing. This process can span months or even years, but offers significant financial gain for the criminal.

Variations of synthetic identity fraud include paying individuals with good credit to link their accounts to the synthetic identity, creating fake digital footprints on social media, and using fake checks to temporarily repay credit lines before maxing them out again.

synthetic IDs: manipulated, blended and manufactured

How to Prevent Synthetic Identity Fraud

While there is very little you can do about a data leak (except minimize the amount of personal information you give out to third-party companies), all of the other exploits can be mitigated with common sense and basic security measures. These include:

Creating unique, complex passwords: you may use a password manager or browser feature designed to create hard-to-guess passwords. Avoiding reusing the same passwords is key to reducing risk.
Be suspicious of online/phone interactions: it’s a sad reality, but any kind of online interaction with strangers could potentially link to social engineering. Exercise due diligence, whether you’re using a dating app, selling items on an online marketplace, or making a payment to a new online store for the first time.
Deploy cybersecurity and fraud prevention tools: antivirus, 2FA, encryption… These tools are easier to leverage than you might think, even for the less tech-savvy.
Regularly review your credit reports: one of the earliest signs of identity theft will be suspicious financial activity. Credit monitoring can help flag inaccuracies and prevent further damage before it’s too late.
Be mindful when handing out PII: any online or offline interaction that requires you to submit personally identifiable information (PII) should be carefully considered, whether it’s a social security number (SSN) or a copy of your passport, or even your full name on social media.
Consider your relatives’ ID: criminals have been known to stoop as low as to steal children’s IDs to open credit lines in their name. Some parents take preventive action by freezing child credit reports.

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Synthetic Identity Fraud Detection

Synthetic identity fraud is notoriously hard to detect, as it is specifically designed to fool anti-fraud measures. However, companies can deploy a number of powerful features to identify users beyond the standard KYC checks.

1. Leverage Digital Footprint Analysis

Online digital footprint analysis is designed to identify people without relying on ID documents or biometrics. It’s a fantastic way to pre-screen for KYC checks, but also to instantly flag potential synthetic ID fraud.

Digital footprint analysis includes email and phone number analysis, which also lets you link data to social media profiles. This has a number of positive results for companies as you can:

Use social media profiles to confirm identities.
Flag users with no social presence
Mark high-risk customers based on the kind of social networks they have joined.

Similarly, customers who sign up with virtual SIM cards, fake phone numbers or invalid email addresses should at least be considered high risk for your business.

2. Extract More Data With IP and BIN Lookups

Another way to spot suspicious identities is to find red flags in user’s alternative data, such as IP addresses or card numbers. In fact, you could even combine both tools to spot inconsistencies, such as a card issued in one country, and an IP pointing to another.

As is always the case with this kind of granular data, it’s not enough to claim an identity is fraudulent. You have to combine as many data points as possible to create a user profile and flag the suspicious ones accordingly.

3. Deploy Device and Browser Fingerprinting

If fraudsters are successful once, they tend to target the same companies multiple times. The challenge for them isn’t to create hundreds or thousands of synthetic IDs; it’s to make it look like each of these is connecting to your site as a new and legitimate user.

A device fingerprinting module can identify the tools that a fraudster relies on to spoof different users and devices. This is done by extracting data relating to their configuration of software and hardware, allowing you to spot:

unique browser setups
suspicious hardware configurations
emulators and virtual machines

Moreover, device fingerprinting lets you create hashes for each unique configuration, which lets you spot connections between users and instantly flag repeat offenders.

4. Identify Fraudulent Behavior via Velocity Rules

Last but not least, it’s not just about looking at data points, but about understanding user behavior. This is particularly important for the more sophisticated attacks, and those perpetrated by money mules who use their real IDs.

In fraud prevention terms, user behavior is identified via velocity rules. These aren’t necessarily complex but can analyze a wide variety of data points, including timeframes.

Here are some examples:

How quickly did the user go through the entire KYC process?
How quickly did they complete the user authentication stage?
Did they enter their social security number in one keystroke?
How many times has a similar browser/hardware setup appeared in the last few days?
How frequently does the user request to raise their credit limit?

Of course, the sky’s the limit with the kind of data you want to examine. But the key here is that you can identify suspicious behaviors, even from fraudsters who have already managed to infiltrate your platform.

5. Enable Machine Learning Suggestions

A whitebox machine learning system is particularly adept at spotting patterns that point to synthetic ID fraud. By extracting all the data mentioned in the points above (device, IP, card, behavior) and feeding it to the system with the right labels, you can expect insights that no analysts could have extracted.

If you are consistent in your reporting and use enough feedback mechanisms, you can begin getting suggestions that may point to the most undercover and sophisticated fraud. Best of all, the efficiency will improve over time, and the rules are based on your own historical business data.

Dangers of Synthetic Identity Fraud in 2024

Synthetic identity fraud is challenging to detect and could cost businesses nearly $5 billion by 2024. It often bypasses KYC checks, leading to fines, reputational damage, and legal issues. This type of fraud also drives up payments and chargeback rates, contributing to 80% of credit card fraud losses and 20% of chargebacks.

For consumers, synthetic identity fraud can result in fraudulent loans, credit damage, and a lengthy, costly process to resolve identity issues.

Conclusion

Criminals have access to a growing number of resources to create synthetic IDs. For targeted companies, it’s not enough to simply implement static ID checks and fraud rules, and leave them to run on autopilot.

However, you don’t have to waste all your resources on intensive manual reviews for identity proofing. Using sophisticated risk tech, you can combine tools to create a net that will filter out bad users, and only allow in those who will help your company reach its goals.

Leveraging an advanced fraud detection tool makes combating this form of fraud more feasible. Below, we’ll delve into these strategies in detail.

Frequently Asked Questions

What are the warning signs of synthetic identity fraud?

If you notice strange payments on your statement or start receiving suspicious emails, it’s possible some of your ID documents have been stolen and used for synthetic IDs.

How do people create synthetic identities?

To create a synthetic identity, you need some kind of real document to begin with. It could be a name, address or social security number. The fraudster then modifies or tweaks the information for their need.

Why do fraudsters use synthetic IDs?

Synthetic IDs are harder to detect than made-up, completely fake IDs because they contain an element of truth (the person’s ID documents). This is why fraudsters use them to bypass KYC checks or for fraudulent transactions, among others.

What kind of stolen data is used in synthetic ID fraud?

Fraudsters rely on any identifiable information they can find, including tax-related information, medical records, social security numbers, and even children’s identity records.

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Digital Footprinting | Browser Fingerprinting | Fraud Detection API | Fraud Detection with Machine Learning & AI

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