Fake account creation is a type of fraud that occurs when bad actors create accounts using false or stolen information. They do this to exploit systems, access services, or commit further fraud, and they can cause serious harm to e-commerce platforms, online marketplaces, social networks, and financial apps. They are also alarmingly common.
In the social media industry, Facebook reported removing 1.2 billion fake accounts in a single quarter of 2024, most of which were deleted before users even flagged them. Fake account creation is also common in the financial sector, with JPMorgan Chase discovering that a recently-acquired college financial aid platform had only 300,000 legitimate accounts instead of the 4.25 million accounts reported. The same report from Forbes detailed an account of Wells Fargo facing fines when the U.S. federal government discovered Wells Fargo employees had opened 3.5 million new accounts using customer data for illicit purposes.
Identity fraud is growing fast, with synthetic identity fraud alone projected to cost the U.S. up to $23 billion by 2030.
Whether for immediate gain or long-term infiltration, fake account creation is often the first step in a broader fraud strategy. To stay ahead, businesses need to detect and prevent fake account creation before it impacts real users or operations. In this article, we’ll cover the basics about what fake account creation is, how to detect fake accounts, and how to prevent them in the long term using best practices and fraud detection solutions like Sift.
What is Fake Account Creation?
Fake account creation refers to the act of registering accounts using false, stolen, or manipulated information, often with the intent to commit fraud, exploit promotions, or mask malicious activity. These accounts may appear legitimate on the surface, but they’re designed to deceive systems, bypass security checks, or gain unauthorized access to services.
Why Do Criminals Create Fake Accounts?
Criminals create fake accounts for a variety of reasons, but are usually tied to fraud and abuse. They may use them to exploit referral programs, send spam, conduct phishing schemes, or commit more serious crimes like account takeover or payment fraud. Fake accounts also serve as a base layer for testing stolen credentials or scaling larger attacks across platforms.
How Are Fake Accounts Created?
Fake accounts are created using methods that range from manual sign-ups with fabricated details to highly automated, scripted processes that can generate thousands of accounts in minutes. Fraudsters often rely on tools like residential proxies, bots, fake email generators, and device spoofing to appear legitimate and evade detection. In more advanced cases, stolen identity data is used to craft convincing, seemingly authentic accounts that are harder to detect without advanced fraud prevention solutions.
How Does Fake Account Creation Work?
Fake account creation can range from simple to highly sophisticated, depending on the fraudster’s goal and technical capabilities. At its core, the process involves submitting falsified or stolen information—such as fake names, email addresses, phone numbers, or identity documents—during the account registration process. Fraudsters often exploit weak verification steps or automated onboarding systems to slip through undetected.
In low-effort attacks, individuals may manually create accounts using random data or disposable emails. However, large-scale fake account creation is typically automated, relying on bots and scripts to generate hundreds or even thousands of fake profiles at once. These tools are often combined with residential proxies, device emulators, and CAPTCHA-solving services to mimic real user behavior and bypass detection.
Some attackers also use synthetic identities, which blend real and fake data to build convincing profiles that can persist over time and be used for more advanced fraud. These accounts are difficult to detect and may lie dormant until they’re needed for a scam, or be sold in bulk on underground markets.
How to Detect and Prevent Fake Account Creation
Preventing fake account creation requires a multi-layered strategy that balances security with user experience so real users can sign up easily, while bad actors are blocked early. Here are key methods to identify suspicious sign-ups:
User Signal Analysis
Monitor user activity during and after sign-up. Unusual patterns, such as completing forms too quickly, using copy-paste shortcuts, or navigating inhumanly fast, can indicate bot activity or automation.
Device and Identity Intelligence
Track device identity, IP addresses, and geolocation using solutions like Sift. Multiple sign-ups from the same device, mismatched locations, user history, or use of proxies and VPNs are strong indicators of fraud.
Email and Phone Number Patterns
Analyze the structure and reputation of contact information. Disposable email domains, randomized strings, and reused phone numbers are often linked to fake accounts. Sift makes this easy by flagging personal information that is tied to suspicious activity or fraud.
Velocity Checks
Look at how many accounts are being created over a short period from the same source. A sudden spike in registrations from one device or IP is a red flag.
Sift’s Global Data Network
Sift is a unique solution that allows businesses to tap into global fraud intelligence to identify known malicious behavior. Cross-referencing activity across platforms can help flag suspicious accounts more quickly.
Real-Time Risk Scoring
This is another Sift speciality. Our AI-powered fraud detection algorithm assigns a risk score at signup based on a variety of signals like user behavior, device data, and traffic patterns. Businesses can use that score to trigger further review, scrutiny, or even denial.
Monitor Post Sign-Up Activity
Continue tracking accounts after creation. Some fake accounts remain dormant or behave like real users at first, only to engage in fraud weeks or months later.
What is the Business Impact of Fake Accounts?
Fake account creation presents a security issue that can affect an organization’s reputation, users, and revenue.
Emerging fraud trends can be difficult to detect before the damage is done. Worldwide, e-commerce fraud, which often involves fake accounts, led to $44.3 billion in losses in 2024, and this figure is expected to soar to $107 billion by 2029.
Fake users burden support teams, increase infrastructure costs, and require constant monitoring. On average, fraud losses amount to almost 3% of annual revenue and require fraud management teams that typically costs around 10% of a businesses income.
Fake accounts can damage your users’ trust in your company, either because they lose confidence in your ability to protect their data or because they are personally affected by a fake account. A report from Harvard claims that Americans lost $12.5 billion to cybercrime in a single year, and users expect modern companies to protect their data and prevent the use of their credentials without permission.
Fake account creation requires dynamic, modern solutions with solid historical data as a backbone, which is where Sift can help.
How Sift Helps Prevent Fake Account Creation
Sift helps businesses prevent fake account creation by using AI-driven, data-backed fraud detection to identify and stop suspicious sign-ups before they can do harm. Rather than relying on static rules, Sift applies machine learning and global threat intelligence to evaluate the risk of every new account as it’s created.
Sift’s Global Data Network adapts in real time using shared signals to quickly get ahead of new fraud patterns. It uses more than 16,000 signals from more than 1 trillion events per year to identify suspicious behavior. Through AI-powered pattern recognition, any one of the signals gathered from a user can tell your business if a user is trustworthy or in need of extra scrutiny.
With Clearbox Decisioning, Sift allows any business to understand why a user was flagged. They can raise or lower risk thresholds based on industry needs to allow legitimate users to have a frictionless experience while stopping fraudsters in their tracks.
The platform also supports advanced automation through custom workflows and real-time risk scoring. This gives fraud teams the flexibility to adapt defenses based on changing tactics, review flagged activity more efficiently, and stop fraud early in the customer journey.
With Sift, businesses can investigate risky accounts, take targeted actions, and reduce downstream threats like payment fraud or account takeover. It’s an unprecedentedly precise, scalable solution that helps businesses stop fake account creation while encouraging growth and user trust.
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