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Guide to Synthetic Identity Fraud: Prevention & Solutions

Your business probably already has a KYC verification process designed to confirm users are who they say they are.

But criminals have found new ways to create convincing-looking identities – and one is synthetic identity fraud, fuelled by the 14 million identities that are stolen each year.

They are combined with fake and generated data to help criminals achieve more – as well as with each other to create new personas. This is what makes fighting synthetic identity fraud so challenging.

But this isn’t as hard as it sounds with the right risk management tools. Let’s break it down below.

What Is Synthetic Identity Fraud?

Simply put, synthetic ID fraud is any fraudulent activity that uses a synthetic ID – a combination of fake and real-person data coming together to form a new identity. This real information is often sourced from stolen identities itself.

A Frankenstein’s monster of an identity, a synthetic ID is always stitched together from various parts of other identities – real or fake. It can also be made of multiple people’s personally identifiable information (PII), without any fake or made-up data. For instance, a real social security number from one person combined with another’s credit card details. 

The applications are myriad, from opening new accounts to bypassing KYC verification checks and getting fraudulent transactions approved.

Synthetic Identity Types

Synthetic IDs can be:

  • Manipulated: combining real user documents and fake, made-up data
  • Blended: combining real information from multiple sources
  • Manufactured: for instance, a social security number that is randomized to fall within the right range
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How Does Synthetic Identity Fraud Work?

Fraudsters create synthetic IDs to bypass various identity checks. Here is an example of how a synthetic ID can be used for bank fraud:

  1. A fraudster buys personal information on the dark web.
  2. They generate fake information and combine it with some of the PII.
  3. They use the resulting identity to apply for a credit card.
  4. They borrow money and repay diligently for a while.
  5. When the limit is raised, they max the card and disappear.
  6. The bank tries to collect the money… and there’s nobody there.

Synthetic identity fraud can be harder to detect than standard identity fraud because it contains elements of real ID documents. These can help pass verification, whereas purely fake profiles are easily flagged.

How Dangerous Is Synthetic Identity Fraud?

Per the 2021 Future of Fraud Forecast, synthetic ID fraud (or synthetic identity theft) is the fastest-growing type of financial crime. Based on Experian’s own definition, it accounts for 80% of credit card fraud losses, and nearly 20% of chargebacks incurred by merchants.

Not only that, but according to the Federal US Reserve, synthetic identity fraud was the fastest-growing type of fraud in the US in 2019 too – while 85–95% of all synthetic ID fraud cases were not flagged by those legacy security systems. According to the same research, it is a particular problem in the US because of the country’s reliance on static personally identifiable information, including Social Security numbers (SSNs).

Synthetic ID fraud can affect any kind of business that monitors its user accounts.

the key challenges and properties of synthetic identity fraud

How to Detect Synthetic Identity Fraud

There’s no magic bullet when it comes to synthetic identity fraud detection. You’ll need a multi-layered approach, ideally combining several technologies that include robust device fingerprinting, data enrichment and digital footprint analysis.

Let’s look at exactly how SEON can help by breaking these down one by one:

With Device 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 those tools that a fraudster will use to spoof different users and devices to give off the appearance of unrelated shoppers. With it, you can instantly flag:

  • proxy usage
  • Tor connections
  • VPN use
  • strange browser setups
  • suspicious hardware configuration
  • emulators

The key here is not just to focus your attention on strange configurations of software and hardware, but also to highlight connections between users.

By frictionlessly logging each device setup as a unique ID, you can notice patterns that could point to bot use, or assign fraud scores to individual IP addresses.

device fingerprint used to detect synthetic identity fraud

Once SEON’s platform has found several users that could be fake accounts of one criminal, the platform flags them in an easy to visualize format:

Customer connections

With Digital Footprint Analysis

Another highly successful technique to stop synthetic ID fraud? Online digital footprint analysis for fraud prevention.

This includes email and phone number analysis, to see if their details appear legitimate – but one of the most effective techniques is undoubtedly social and online platform lookup.

With a reverse email address or phone number search, you can see whether the user’s digital footprint looks legitimate.

SEON can check 50+ social media networks and online platforms and a growing number of platforms in emerging markets. This has three key benefits:

  1. You can use their social media profiles to confirm their identity.
  2. An absence of an online footprint may point to fraud.
  3. The types of social media networks users are subscribed to can also help with credit scoring.
  4. Granular information sourced, such as the number of Airbnb trips taken, can point to high-value customers or the exact opposite – high-risk customers.
digital footprint

With Behavior Analysis 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.

At SEON, this is examined via custom rules, machine learning rules and 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.

A whitebox machine learning system is particularly adept at catching matching behavior from fraudsters who have passed the KYC stage.

If you are consistent in your reporting and use enough feedback mechanisms, you can begin understanding behavioral patterns that may point to the most undercover and sophisticated fraud.

velocity checks

Synthetic ID Fraud vs Traditional ID Fraud

Traditional identity fraud is perpetrated in real-time. Sending phishing emails from a hijacked account, for instance, constitutes an example of direct identity fraud. 

Synthetic identity fraud, however, tends to be cultivated over time by more sophisticated criminals.

The goal is to fly under the radar for as long as possible, as they want to create an account and use it in the long term. This is important because it highlights a key challenge in fighting this kind of fraud.

The criminals who rely on these techniques are patient, calculated, and sophisticated. But they also tend to be organized, which we can use against them to our advantage. 

What Kind of Stolen Data Is Used in Synthetic ID Fraud?

Identity theft and fraud go hand-in-hand. Criminals will stop at nothing to acquire records that help them create fake profiles. This includes stealing:

  • Tax-related information: In the US especially, tax information from the IRS can be used to recover extra personal data.
  • Medical identity theft: Medical information is also often used to apply for prescription drugs or to file insurance claims under someone else’s name.
  • Child identity theft: Proof that fraudsters will stoop as low as they can: Children’s records are often used to apply for credit cards or online loans. This works because their credit scores are either high or nonexistent, and it will take many years before anyone realizes the information was compromised.

This type of fraud is on the rise because fraudsters have access to a growing number of options and tools to access stolen identities and to generate new, synthetic ones.

Data Breaches Mean IDs Are Easier to Source

Sourcing ID documents is child’s play for fraudsters. They can hop on the dark web and purchase huge lists from leaked databases, at surprisingly competitive rates.

Interestingly, existing email data breaches also help conduct passive identity verification at SEON.

However, as it leads to identity theft and identity fraud, such a data leak is likely to cause a vicious cycle of account takeovers, fake account openings, and a rise in the number of synthetic IDs.

More People Are Willing to Sell IDs

Adding to the challenge of widely available stolen documents, many people willingly sell or rent out their IDs in exchange for a fee. 

Fraudsters offer to buy personal details or to borrow people’s bank accounts to enable synthetic identity fraud. Here are a few options:

  • Money mules: A money mule is a person who transfers stolen money on behalf of others. This is also referred to as “smurfing” or “squaring”. Under 25s are particularly at risk. Money mules may find themselves complicit in money laundering schemes.
  • Bank drop: A bank drop is the account that money mules will use to receive and transfer illicit funds.
  • Rent-an-ID: In the underground economy, we’ve seen a proliferation of services that blatantly ask people to rent out their documents, in exchange for payment. Collaborators often realize once they’ve shared their fullz (their details), they can never stop criminals from using them, no matter what their agreement was.

The takeaway? There’s no shortage of resources available to stitch together the perfect synthetic ID, tailored to defraud your online services.

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The Rise of Fake ID Services

What if fraudsters run into heavier KYC checks in the form of document uploads?

This is barely an inconvenience. They can simply purchase a document from a forging service – which are plentiful, affordable, and surprisingly effective.

Can’t provide the right documentation? No problem. A growing number of clearnet services also photoshop IDs for fraudsters, helping them bypass KYC checks using photo IDs.

Better Detection With a Multi-Layered Approach

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.

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.

You might also be interested in reading about:

Learn more about:

Data Enrichment | Browser Fingerprinting | Fraud Detection API | Fraud Detection with Machine Learning & AI

Related Source for this article:

  • BBC: I was a teenage ‘money mule’
  • Federal Reserve: Synthetic ID Fraud in the US Payment System
  • Comparitech: Identity theft facts & statistics: 2019-2022
  • Experian: 2021 Future of Fraud Forecast

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Author avatar
Bence Jendruszak

Bence Jendruszák is the Chief Operating Officer and co-founder of SEON. Thanks to his leadership, the company received the biggest Series A in Hungarian history in 2021. Bence is passionate about cybersecurity and its overlap with business success. You can find him leading webinars with industry leaders on topics such as iGaming fraud, identity proofing or machine learning (when he’s not brewing questionable coffee for his colleagues).

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