There is always a race between Money launderers/fraudsters and financial institutions to be one step ahead of each other.

As the Financial Institutions (FI) upgrading their AML programs to check the ML/fraud activities, the Money launderers keep working on finding new ways to launder the money or new ways of fraud.  One such way is using Synthetic Identity Fraud.

Synthetic identity theft is a type of fraud/illegal activity in which a criminal combines the fake and real information to create a new identity.

For example, the synthetic identity may have a real, delivery address and the SSN may appear valid, but the SSN, name, and date of birth combination do not match with any one real person.

How fraudsters create Synthetic Identity

  1. Obtaining the stolen SSN or PII over the dark net is not that tough. One can buy such information for a small payment. If you search dark net or dark web on google you will get to know that it is mostly used by criminals and other anti- social elements. Please see the report from security firm Flashpoint (PCMag), you will get to know that it is quite easy and inexpensive purchase a social security number on the dark net.
  2. Fraudsters uses these newly formed identity and apply for credit/loan and since these identity does not exist in the system, so there is no credit history when they are promoted for credit check by agency and its  credit/loan will be denied. However, the act of applying for credit automatically creates a credit file at the bureau in the name of the synthetic ID, so the fraudster can now set up accounts in this name and begin to build credit.The fact that the credit file looks identical to those of many real people who are just starting to build their credit record—that is, there is limited or no credit history—makes the scam nearly impossible to detect.

It can take months, even years, to build up good credit with small purchases that they promptly pay off, continuing to legitimize the identity. Over time, they increase their available credit with new cards and higher limits, which gives it a legitimate look.

Once there is an opportunity for a high payout, the synthetic identity maxing out all credit and disappearing. 

Preventive measures

It is important to identify SIF during the consumer onboarding phase and FIs need to enhance the onboarding process

  1. Should rely on Government-issued IDs and pictures for photo evidence which can be uploaded. This documentation along with biometric authentication (identification verification using biological input by scanning or analyzing voice, fingerprints, or retinas), location information, and IP addresses make strong identity verification platforms.
  2. Utilizing third-party vendors that screen activity for multiple institutions provides an additional level of identification capabilities. Analysis of data on a nationwide or global scale finds correlations between synthetic identities more quickly than can individual institutions searching its own data and transactions. 
  3. Retroactive Identification to understand the true source of past credit losses, provide the ability to forecast for future losses and to ensure regulatory requirements are met, financial institutions are conducting retroactive data studies to analyze charge-off data. 
  4. By screening out people with lower credit risk scores who might be expected to charge-off, FIs are able to zero in on anomalies that identify intentional fraud. For example, a customer, aged 55, with a credit short history dating back only five years is a red flag for synthetic identity fraud.

Conclusion

According to Forbes’ report here. There is no doubt that Synthetic identity fraud is a relatively recent phenomenon that is on the rise. McKinsey claims synthetic ID fraud is the fastest-growing type of financial crime in the U.S. LexisNexis Risk Solutions (via Yahoo Finance) found that “61% of fraud losses for [large] banks stem from identity fraud [and] 20% of the identity fraud incurred by these larger banks is synthetic identity fraud.”

The Federal Reserve reported that the largest synthetic ID ring detected to date caused $200 million (or more) in losses from 7,000 synthetic IDs and 25,000 credit cards. Synthetic fraud costs lenders more than $6 billion annually, and the average loss is estimated at $10,000 per account.

Disclaimer: I have used various internet sources and my knowledge to collaborate in above article.

Shobhit Garg
I am an Indian Citizen and currently based in India. I am CAMS (Certified Anti-Money Laundering Specialist) certified professional, started my career with one of the largest AML software company called Mantas(Now Oracle) and have nearly 9+ years of experience in AML/Mantas. I have worked with ABN AMRO and CITI bank as Mantas(AML) SME. I always try to keep updated myself with latest trends in AML field and regularly follows the various known organizations/bodies in the AML field.