How to Stop Synthetic Identity Fraud with AI-Powered Detection

Find out how artificial intelligence is powering advanced defenses against the growing threat of synthetic ID fraud.

The digital financial ecosystem is experiencing an increase in the ferocity and frequency of synthetic identity fraud. This type of scam, also known as synthetic identity theft, involves criminals who use false credentials combined to create synthetic identities that are not related to a real person. To generate these false identities, fraudsters use fictitious personally identifiable information (PII) combined with potentially legitimate social security numbers (SSNs).

This technique was meant to improve public safety, but it has unintentionally made it more difficult for fraud detection systems to identify counterfeit SSNs. Moreover, not all synthetic identities are made equally. The technique scammers use to establish these false personas affects both companies’ decision-making and the difficulty of identifying this particular type of fraud.

This is why a comprehensive AI-based fraud detection solution is an essential tool for businesses across different online industries.

Synthetic Identity Fraud: Current Landscape

Synthetic identity fraud involves the theft of a genuine individual's Social Security number (SSN), which is then paired with fabricated personal details such as name, date of birth, mailing address, email account, and phone number to construct a fraudulent identity.
Detecting synthetic identity theft poses challenges for conventional fraud monitoring systems. Particularly vulnerable to this type of fraud are children, the elderly, and homeless individuals, who are less inclined to utilize credit services or promptly monitor their credit history.

How Synthetic Identity Fraud Works

Fraudsters employ various methods to fabricate synthetic identities. One approach involves obtaining a person's SSN through theft or purchasing a stolen SSN from the dark net or other illicit online platforms. This legitimate SSN is then merged with fictitious personally identifiable information in what's termed identity compilation.

Another tactic involves stealing genuine personal information from individuals, making slight alterations, and presenting it as a new identity. This particular technique refers to identity manipulation.

Alternatively, criminals may engage in identity fabrication, where they create a false identity from the bank using counterfeit personally identifiable credentials.

This is how synthetic identity fraud works:

  1. Synthetic Identity Creation. The first phase supposes amalgamating genuine identity details with fabricated information to construct a counterfeit persona, commonly known as "Frankenstein identity." Sometimes, hackers steal real SSNs or purchase them on the dark web. Typically, fraudsters target SSNs that belong to individuals without established credit histories, such as children, homeless individuals, or the elderly. They proceed to assemble a profile around this SSN, incorporating fictitious names, dates of birth, and other details.
  2. Online Credit Application. In the next stage, criminals utilize the freshly crafted synthetic identity to apply for credit via online platforms. Generally, a lender forwards it to credit bureaus for verification. As a rule, this initial application is declined, given the absence of any credit history associated with the synthetic identity. Nonetheless, the mere submission of the application initiates the creation of a credit file.
  3. Second-Chance Application. A fraudster continues persistently submitting credit applications across multiple financial institutions until it is eventually approved. Often, this initial approval comes from a high-risk lender. Over time, they can expand their access to credit by gaining approval from lower-risk lenders and securing higher credit limits. They carefully cultivate and maintain this newly established credit profile over months or even years. This is how they develop a positive credit score.
  4. Vanishing or Busting Out. Lastly, scammers gain access to increasingly larger credit limits. Eventually, they execute what's known as a "bust out" strategy. This involves reaching the maximum credit limit and then ceasing payments before vanishing. Alternatively, the fraudster may attempt to double their gains by alleging identity theft to have charges removed. Another tactic involves using counterfeit checks to clear the balance before maxing out the credit line for a second time.

The main problem here is that businesses find it hard to detect synthetic identity theft. This is mainly due to wrong fraud interpretation. Some companies tend to misclassify it.

Identity Theft Vs Identity Fraud: The Difference Explained

Identity theft involves the theft of someone's personal information. Meanwhile, identity fraud is associated with a stolen identity (not even authentic) needed to perpetrate fraudulent activities. The main risk for businesses arises when criminals leverage stolen or fabricated identities to obtain loans and abscond with the funds. Sometimes they use it to establish fake accounts and accrue charges. Additionally, they may exploit stolen or fake identities to take advantage of medical or employment benefits.

Other Cases of Using Synthetic Identity Theft

Synthetic fraud is frequently exploited for financial scams. Hackers leverage synthetic identities to secure various financial products such as loans, bank accounts, and credit cards. They may also engage in activities like filing fraudulent tax returns, obtaining medical services, or applying for unemployment benefits, all under a false identity.

What’s more, synthetic identities provide fraudsters with numerous opportunities for other kinds of illicit activities. For instance, organized crime syndicates can utilize false accounts linked to synthetic identities to manage or launder unlawfully acquired funds. Criminals might exploit a synthetic identity to secure a personal loan, default on payments, etc.

The Damage Caused by Synthetic ID Fraud

Synthetic fraud appears to be a significant challenge for financial institutions due to its costly and elusive nature. Fraudsters employ intricate tactics to evade conventional fraud detection approaches, making fraud detection extremely complicated.

Financial institutions must also recognize the misclassification issue associated with synthetic identity theft. In cases of default, lenders may mistakenly associate synthetic identity fraud with a credit loss. This presents a problem that results in growing costs for companies and risks for individuals.

Costs for Companies

Companies lose billions of dollars due to synthetic identity fraud. What’s more, this type of scam calls for considerable resources in pursuit of non-existent individuals. According to a report, online lenders lose $6 billion annually due to synthetic identity fraud, prompting the Federal Reserve to label it as the fastest-growing financial crime.

Individual Risks and Repercussions

The efficient approach to fraud detection represents a major challenge in combating synthetic identity theft. Fraudsters craft identities that closely resemble legitimate ones, complicating the task of lenders and banks in identifying suspicious accounts. Moreover, thieves often prey on individuals who infrequently monitor their credit accounts, further delaying the detection of any fraudulent activity.

Evidently, synthetic identity fraud presents an escalating risk. Businesses need to quickly adopt more effective methods to verify the identities of their users. Given the proliferation of data breaches, compromised personally identifiable information (PII), and the accessibility of the dark web, relying solely on social security numbers and credit bureaus is no longer sufficient. Traditional tools designed to combat identity fraud are inadequate in detecting synthetic identity fraud.

Juicyscore’s Solution Against Synthetic Fraud

JuicyScore harnesses cutting-edge machine learning and artificial intelligence technologies to provide a robust fraud prevention service. Tailored to the needs of businesses, it offers a comprehensive range of solutions designed to detect and prevent unauthorized service manipulation using synthetic identity.

Leverage AI for Predictive Analytics

Our solution comes with advanced built-in technological improvements driven by ML and AI-based innovations:

  • The software ensures efficiency and consistency through advanced AI-driven algorithms. By utilizing hundreds of distinct criteria for device authentication, it accurately generates detailed end-user profiles from diverse technological data and behavioral attributes.
  • Employing ML-powered techniques, the system autonomously identifies and addresses fraud risks in real time. It leverages hundreds of user device settings, features, and indicators to analyze technical data, enabling seamless monitoring, assessment, and analysis of network infrastructure.

Enhance Fraud Defenses with Broad Data Feeds

This consolidated data vector enables automated data analysis. Despite its lightweight nature, it adeptly handles extensive information, evaluating the architecture of operating systems, client accounts, and the connectivity of Android and iOS devices. Its main goal is to assist organizations in improving the efficiency of their decision-making procedures.

Key Features

Our system places a high priority on safeguarding user privacy. In simpler words, we do not collect any personal data. Instead, the solution concentrates on processing and analyzing behavioral and device-related parameters, making it simpler for businesses to sort out potentially risky and fraudulent applications.

Device Profiling

Our solution meticulously examines various primary and secondary attributes linked to potential fraudulent activities. JuicyScore closely monitors essential device-related data to attain the highest level of accuracy in device fingerprinting.

To ensure exact fingerprinting, our data vector examines crucial elements including RAM capacity, screen size, display quality, device classification (tablet, desktop, laptop, or mobile), and other pertinent factors.

Behavior-Based Protection

Upon detecting any anomalies associated with synthetic fraud red flags, the system immediately notifies business owners. Teams then pinpoint suspicious behaviors such as randomization, device cloning, remote access, and other routing tactics. By leveraging behavioral patterns, the software aids in promptly identifying and thwarting various forms of potentially risky or fraudulent activities.

How It Works

JuicyScore leads the way in anti-fraud technology, catering advanced anti-fraud instruments to numerous industries. We consistently enhance our solutions to meet the evolving demands of digital business platforms.

Seamless Customers’ Authentication

The system evaluates behavioral and technological data sets, employing ML and AI technology to continuously improve its fraud detection algorithms. Processing involves analyzing various aggregated criteria, including dwell/flight times, average typing or content reading speeds, extended device usage from the same source, duplicated or randomly chosen devices, and other behaviorally relevant data.

Instant Alerts for Suspect Registrations

JuicyScore can swiftly alert businesses to any detected suspicious activities, indicating potential fraud through "red flags. Businesses assess the results of the scoring model and decide on the appropriate actions for each specific scenario. JuicyScore aids in enhancing risk assessment strategies.

Custom Integration with KYC and AML Processes

Custom integration offers businesses the flexibility to tailor their compliance processes to their specific needs and operational workflows. By integrating KYC and AML functionalities directly into their existing systems, organizations can streamline compliance efforts and mitigate risks associated with financial crimes.

One of the key benefits of custom integration is the ability to automate repetitive tasks, such as identity verification and transaction monitoring. This not only improves efficiency but also enhances accuracy and minimizes the risk of human error.

Get Started with JuicyScore Today

Our professional team is here to assist you at every step of JuicyScore implementation. Explore the benefits of advanced anti-fraud technology with seamless integration in just three simple steps:

  1. Incorporate JavaScript or SDK into your platform.
  2. Tailor configurations to align with your business needs.
  3. Commence data collection and analysis within moments.

Reach out to us today to arrange a demo and discover more about our offering.


What is synthetic identity fraud?

Synthetic identity fraud entails stealing a real person's Social Security number (SSN) and combining it with fictitious personal information like name, date of birth, address, email, and phone number to create a fraudulent identity.

What techniques do fraudsters use to create synthetic identities?

They cultivate fake identities from scratch to build up a positive credit score and then apply for larger credit limits. Synthetic identities are also used to leverage healthcare, employment, and other social services.

How can AI and machine learning detect synthetic ID fraud?

ML and AI technologies bring the capability to adjust to changing fraudulent behaviors and draw insights from past business encounters. This entails utilizing historical data alongside contemporary anti-fraud methodologies to empower security measures.

What suspicious user behaviors may indicate synthetic ID fraud?

The signs of synthetic fraud taking place include a significant volume of unsecured debt, which typically demands less documentation compared to a mortgage, and a considerable frequency of recent credit inquiries, possibly suggesting an attempt by the fraudster to rapidly establish credit.

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