Rippleshot Blog

Synthetic Identity Fraud Data: Why Financial Institutions Should Care

Posted by Anna Kragie on 30, Mar, 18

Synthetic fraud isn’t a new phenomenon, but with an increase of incidents across the fraud ecosystem — from credit card fraud to identity theft — this problem isn’t going away anytime soon.

In fact, it’s estimated that Synthetic ID fraud accounts for 85% of all identity fraud in the U.S., and continues to rise annually. A report from TransUnion reported that between 2016’s Q4 and 2017’s Q4, suspected synthetic fraud balances rose 5.2%. Collectively, this has become a $290 million problem.

In the financial services world, synthetic fraud continues to be a top concern since it’s getting increasingly difficult to keep up with. Synthetic fraud allows hackers to set up accounts in a person’s name that appear to be authentic, but are in fact fictitious. The construction of new synthetic IDs is based on combining truthful and false information to build a credit file and then open new accounts, which is perpetrated at scale by opening hundreds of new accounts.

“The threat of online fraud is significant as faceless digital application channels can make it more difficult to assess the veracity of the identity being used to acquire credit,” Geoff Miller, head of global fraud and identity solutions for TransUnion, said in a statement.

“High tech fraudsters armed with real personal information on good consumers apply with multiple identities for multiple products with multiple lenders within hours or days.”

Why Financial Institutions Should Care

The outstanding balances from of suspected synthetic fraud identities also saw an increase last year. When comparing 2017’s Q4 to the year prior, these balances rose 6.6% to $885.42 million. These figures come from auto loans, credit cards, personal loans and retail cards combined.

For financial institutions, they often become the real victim of synthetic fraud. Since there is no reported fraud from an actual person, or an actual person to trace the fraud back to, they often end up absorbing the costs. For example, when a credit card is created with a synthetic ID, the balance will never be paid as there is no specific person for the bank to collect from.

This is why banks have placed more emphasis on early fraud detection to alert them of suspicious behavior, and why banks are working to add more verification and authentication to online banking. More fraud detection and security protocols in place can help reduce risks to financial institutions.

Another important stat for financial institutions to take note of comes from a TransUnion survey that indicated that 63% of consumers wouldn’t do businesses with a financial institution if they had previously been declined for applying for credit. In these instances, a fraudster who used someone else’s identity to open a line of credit could cause a trail of problems for that particular consumer. Not only would it be harder for that person to open a credit card, but that financial institution also gets caught in the trap of rejecting a legitimate customer.

The Rise of Synthetic Identity Fraud

Synthetic ID fraud has grown increasingly popular for cyber criminals because of the bigger payoff. The financial gains are much greater since the fraudsters create an identity that is harder for banks to crack down on since there is no actual person to make a complaint over fraudulent activity. Because of this, it’s up to the bank’s own fraud detection mechanisms to spot suspicious behavior.

Since fraudsters don’t need as much personal information as credit card fraud, cyber criminals have shifted their attention to this type of fraud. For example, by combining a legitimate SSN with a fake name, or by using a inactive social security number with a real name, or even a fake name and SSN, an entirely new identity can be created. From there, fraudsters begin to open up lines of credit and credit cards under these synthetic identities.

Where Machine Learning Fits Into the Synthetic Fraud Equation

Machine Learning allows banks to thwart off breach threats faster, detect breaches when they occur, and devise a plan of attack for when breaches hit — preventing them from spreading into even bigger problems. With a rise in identity theft, this is more important than ever.

Not only can machine learning technology process billions to trillions of data, analyze millions of variables, it has the ability to learn and improve everyday, far faster than human analysis. Machine learning can help fraud teams at banks and credit unions be better equipped to get ahead of the problem before it spreads. By eliminating costly, manual processes that are also far less accurate (not to mention far slower), this presents endless benefits for financial organizations.

Having the ability to sift through organized data that’s collected using powerful software, instead of relying on a team of data scientists to interpret what accounts have been breached, and how they were breached, is the only way to devise sustainable business practices and be fully prepared to fight fraud.

Topics: Fraud