Criminals have found new ways to create convincing-looking identities – and one is synthetic identity fraud, fueled 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?
Synthetic ID fraud happens when criminals use a combination of fake and real-person data to exploit a business. It is a form of identity theft and includes a wise range of criminal activities such as bypassing KYC verification checks, abusings promos and bonuses, buying goods with stolen credit cards, or even laundering money.
The 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.
Regardless of what data points the synthetic IDs are made of, the people who create them should always be considered high-risk. It is crucial to flag them as soon as possible as synthetic identity fraud is usually just the first step in more elaborate fraud schemes.
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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:
- A fraudster buys personal information on the dark web.
- They generate fake information and combine it with some of the PII, for instance a social security number.
- They use the resulting identity to apply for a credit card.
- They borrow money and repay diligently for a while.
- When the limit is raised, they max the card and disappear.
- 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?
One of the biggest dangers of synthetic identity fraud is that it is hard to catch. As reported by Forbes, synthetic identity fraud could cost businesses nearly $5 billion by 2024.
More often than not, the end goal is to bypass KYC screening, which may damage your business in numerous ways, including:
- KYC and compliance fines
- Loss of trust and brand reputation
- Legal issues with the rightful ID holders
Moreover, synthetic identity fraud also impacts payments and chargeback rates, when fraudsters make payments in someone else’s name.Based on Experian’s research, synthetic identity fraud accounts for 80% of credit card fraud losses, and nearly 20% of chargebacks incurred by merchants.
Last but not least, for consumers, the consequences are even direr, particularly in countries that realy heavily on static personally identifiable information PII). In the US, for instance, Social Security numbers (SSNs) are highly valued by fraudsters, as it allows them to usurp someone’s identity with relative ease.
Should a fraudster use your ID as part of a synthetic identity, you may have to deal with loans taken out in your name, lower credit score, and, at worse, criminal convictions.Fighting to clear your name of these misdeeds is also costly, both in terms of lost resources and emotional damage. It may take months or even years before you can finally convince the legal system that a fraudster targeted you using your ID details – up to 200 hours, according to a report from the SANS Institute.
Steps for 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.
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 footpris 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.
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.
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.
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.
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, behaior) 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.
Tips to Protect Yourself From 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.
Synthetic Identity Fraud Trends in 2023 & Beyond
This type of identity fraud is on the rise because fraudsters have access to a growing number of options and tools to access stolen identities and generate new, synthetic ones.
When Experian released its Future of Fraud Forecast in January 2023, it predicted that synthetic identity fraud – what the company calls the use of a “Frankenstein” identity – will continue to be a significant threat.
In fact, synthetic ID fraud is likely to be a mainstay over the years that follow 2023, especially when you consider the ease with which fraudsters can access resources that will help them carry out such a crime.
Let’s take a look at some examples of the fraud trends making synthetic ID fraud such a problem.
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? 2023 continues to show that there’s no shortage of resources available to stitch together the perfect synthetic ID, tailored to defraud your online services.
What’s more, without the proper precautions in place, it will sadly continue indefinitely. In fact, the not-too-distant future can be outright unsettling to think of if our increasing reliance on biometrics can be exploited by fraudsters.
Consider, for example, that the IEEE (Institute of Electrical and Electronics Engineers) has already expressed its concerns about how biometric information that lands in the wrong hands could continue to help criminals to use deepfakes to trick authentication checks, approve unwanted payments, and more.
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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.
Synthetic Identity Fraud 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
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.
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.
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.
Fraudsters rely on any identifiable information they can find, including tax-related information, medical records, social security numbers, and even children’s identity records.
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
You might also be interested in reading about:
- SEON: Best Fraud Detection Tools & Software
- SEON: Guide to Ecommerce Fraud Detection
- SEON: Digital Onboarding Solutions & Tools Guide
Learn more about:
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: Experian’s 2023 Future of Fraud
- IEEE: DeepFake Detection for Human Face Images and Videos: A Survey
- Forbes: Socure Report Examines Rise Of Synthetic Identity Fraud
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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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