Recently, I shared articles about the problems surrounding third-party and first-party fraud. Now I’d like to explore a hybrid type – synthetic identity fraud – and how it can be the hardest type of fraud to detect.
What is synthetic identity fraud?
Synthetic identity fraud occurs when a criminal creates a new identity by mixing real and fictitious information. This may include blending real names, addresses, and Social Security numbers with fabricated information to create a single identity.
Once created, fraudsters will use their synthetic identities to apply for credit. They employ a well-researched process to accumulate access to credit. These criminals often know which lenders have more liberal identity verification policies that will forgive data discrepancies and extend credit to people who appear to be new or emerging consumers. With each account that they add, the synthetic identity builds more credibility.
Eventually, the synthetic identity will “bust out,” or max out all available credit before disappearing. Because there is no single person whose identity was stolen or misused there’s no one to track down when this happens, leaving businesses to deal with the fall out.
More confounding for the lenders involved is that each of them sees the same scam through a different lens. For some, these were longer-term reliable customers who went bad. For others, the same borrower was brand new and never made a payment. Synthetic identities don’t appear consistently as a new account problem or a portfolio problem or correlate to thick- or thin-filed identities, further complicating the issue.
How does synthetic identity fraud impact me?
As mentioned, when synthetic identities bust out, businesses are stuck footing the bill.
Annual SIF (synthetic identity fraud) charge-offs in the United States alone could be as high as $11 billion. – Steven D’Alfonso, research director, IDC Financial Insights1
Unlike first- and third-party fraud, which deal with true identities and can be tracked back to a single person (or the criminal impersonating them), synthetic identities aren’t linked to an individual. This means that the tools used to identify those types of fraud won’t work on synthetics because there’s no victim to contact (as with third-party fraud), or real customer to contact in order to collect or pursue other remedies.
Solving the synthetic identity fraud problem
Preventing and detecting synthetic identities requires a multi-level solution that includes robust checkpoints throughout the customer lifecycle.
During the application process, lenders must look beyond the credit report. By looking past the individual identity and analyzing its connections and relationships to other individuals and characteristics, lenders can better detect anomalies to pinpoint false identities.
Consistent portfolio review is also necessary. This is best done using a risk management system that continuously monitors for all types of fraudulent activities across multiple use cases and channels. A layered approach can help prevent and detect fraud while still optimizing the customer experience.
With the right tools, data, and analytics, fraud prevention can teach you more about your customers, improving your relationships with them and creating opportunities for growth while minimizing fraud losses.
To wrap up this series, I’ll explore account takeover fraud and how the correct strategy can help you manage all four types of fraud while still optimizing the customer experience. To learn more about the impact of synthetic identities, download our “Preventing Synthetic Identity Fraud” white paper and call us to learn more about innovative solutions you can use to detect and prevent fraud.
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