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Synthetic identity fraud is the creation of fake identities combining factual and fabricated details. As consumers grow their digital footprints across devices, channels, and geographies, differentiating malicious synthetic behavior from that of good consumers is harder than ever. Traditional rules-based systems and third-party fraud solutions fall short in solving this pervasive fraud.

Solutions to synthetic identity fraud include robust identity verification processes, enhanced data analytics, machine learning-based fraud detection, biometric authentication, and collaborative efforts between organizations and regulatory bodies. Investing in effective solutions and synthetic identity fraud tools is essential to protect your business and its customers from unnecessary risk.

What are the key features of synthetic identity fraud solutions?

Synthetic identity fraud solutions encompass comprehensive tools and strategies that blend cutting-edge technologies with robust fraud detection methodologies and proactive prevention measures. Key features include advanced identity verification techniques using biometrics and machine learning algorithms, robust data analytics to uncover unusual patterns and anomalies, real-time monitoring for early detection of suspicious activities, and collaborative efforts among institutions to share information and strengthen fraud prevention. Additionally, these solutions often use adaptive authentication processes that continuously evolve to stay ahead of emerging threats. It’s critical to use a solution that can do this while providing comprehensive customer education and support to enhance overall security in the financial ecosystem.

Socure’s Sigma Synthetic Fraud solution uses proprietary algorithms to “think” like a fraudster by ingesting cleanly labeled feedback data into the model to train the system and accurately predict and detect evolving synthetic identity fraud threats. Socure’s solution captures 71% of synthetic identity fraud for the top 3% of riskiest users, making it the most effective solution for your synthetic identity fraud detection needs.

How do solutions detect synthetic identity fraud?

Synthetic identity fraud solutions employ a multifaceted approach to detect synthetic identity fraud, harnessing advanced technologies like data analytics, machine learning, and behavioral analysis. These comprehensive fraud detection solutions analyze patterns and anomalies in vast datasets, enabling the identification of subtle irregularities in applicant information. Machine learning algorithms can identify hidden patterns and typical characteristics associated with synthetic identities, while behavioral analysis assesses deviations in user behavior. This multi-dimensional approach empowers organizations to scrutinize applications and transactions more closely, improving their ability to detect and thwart synthetic identity fraud schemes effectively.

What is the false positive rate of synthetic identity fraud solutions, and how is it managed?

The false positive rate of synthetic identity fraud solutions represents the proportion of legitimate transactions or applications incorrectly flagged as fraudulent. This metric is crucial in evaluating the efficacy of fraud detection systems because a high false positive rate can result in unnecessary friction for genuine users, potentially leading to customer dissatisfaction and loss of business.

To manage and mitigate the false positive rate, organizations employ a combination of strategies, including fine-tuning machine learning models, refining rule sets, and implementing adaptive algorithms that continuously learn and adapt to emerging fraud patterns. Additionally, comprehensive risk assessment and context-aware authentication can help reduce false positives, ensuring that legitimate transactions are not needlessly impeded while maintaining high fraud detection accuracy. Balancing a low false positive rate with effective fraud detection is vital to a successful synthetic identity fraud solution.

The industry standard typically aims for a low threshold to balance effective fraud detection while minimizing unnecessary disruptions to legitimate transactions. However, Socure’s solution is smarter. With a high fraud capture rate and low false positive rate, Socure improves the customer experience for non-fraudulent users while ensuring fraudulent activity is stopped. For example, Socure reduces manual reviews by 90%, combined with a 13x reduction in false positives, compared to competing solutions. Socure’s solution has realized up to 90% reduction in client manual review, per testing and live production results, and has market-leading verification of up to 94% of Gen Z consumers, with at least 70% verification of 18-year-old consumers. Socure doesn’t just protect your customers’ identity and money; it safeguards the entire customer journey, reducing friction, and approving more good customers.

Learn More About The State of Synthetic Identity Fraud

How does synthetic identity fraud differ from traditional identity theft?

Synthetic identity fraud differs from traditional identity theft because it combines factual and fabricated details, making it harder to detect. It involves clever combinations of real and phony attributes, such as email, phone, and address– including possible real attributes that don’t correlate to each other– to assemble a new identity. The bad actor may even keep real, legitimate credit history for a time, then max out credit lines or cards at some point to establish a background. Synthetic identity fraud requires substantial work, but for fraudsters, the payout is enormous. It's estimated that $20 to $40 billion is lost to synthetic identity fraud each year!

What is Deepfake synthetic identity fraud?

Deepfake synthetic identity fraud involves using deep learning and AI technologies to produce compelling fake identities, including lifelike images and voices. These deepfake identities are used for fraudulent activities, making detection even more challenging.

Few synthetic identity fraud tools are positioned to tackle the challenge of deepfakes. However, Socure has a unique deepfake positioning that uses generative AI to create synthetic datasets that mimic fraud patterns to enhance existing datasets, which results in a more robust training model. Synthetic data is beneficial for optimizing the mix of good to bad examples in training data, and it can also reduce model bias and protect the privacy of consumers’ PII.

Deepfake detection is the most critical application for synthetic datasets at Socure. We use Predictive DocV to generate deepfake image examples as a basis for a classifier model to detect deepfake images. This is an instance of using a generative AI-powered solution to fight AI-powered fraud.

What are the different ways a synthetic identity can be used to defraud banks or lenders?

There are numerous ways that synthetic identities can be leveraged for fraudulent purposes. Some of the common uses include:

  1. Applying for loans or credit cards with a synthetic identity and defaulting on payments.
  2. Building fake credit histories to qualify for larger loans or better interest rates.
  3. Using synthetic identities to launder money through financial institutions.
  4. Creating multiple synthetic identities to maximize fraudulent loan applications.
  5. Colluding with others to engage in identity layering, where synthetic identities are used in a complex network of transactions to obscure illicit funds.
  6. Obtaining multiple loans simultaneously to default on them.
  7. Manipulating credit scores by strategically adding synthetic identities as authorized users on legitimate accounts.
  8. Applying for mortgages with synthetic identities and falsified income documentation leads to mortgage fraud.
  9. Circumventing identity verification processes to open bank accounts and engage in fraudulent transactions.
  10. Committing check fraud by using synthetic identities to write bad checks, causing financial losses for banks.

What proactive measures can individuals and organizations take to prevent synthetic identity fraud?

Individuals and organizations can prevent synthetic identity fraud by implementing robust identity verification processes, monitoring suspicious activities, and staying updated on the latest fraud prevention techniques. Choosing a robust synthetic identity fraud detection and prevention solution is vital. This is especially important for financial institutions and organizations that engage in B2B transactions– not only can synthetic identity fraud solutions keep your money safe, but they can also help your organization follow regulatory measures and compliance requirements.