HOW TO FIGHT AUTO LENDING FRAUD

Anti-theft technology has reduced the incidence of traditional auto theft. So thieves have shifted to auto lending fraud. If you want to acquire a car today, the internet and an array of services can help put a car in your driveway (or an internationally-bound shipping container) without the need to dirty your hands. False identity, fake pay stubs, even phone numbers that verify employment at some fictitious company make it easy to acquire vehicles without great effort.  

Auto Lending Fraud: Be Prepared

Publications regularly call attention to auto lending fraud. Forbes explains Why Auto Loans Look Like Low-Hanging Fruit To Identity Thieves. Auto Finance News reports fifteen credit unions across the US were targets of an auto lending fraud scheme. Automotive News, citing TransUnion’s proprietary fraud data, states that auto loan balances with suspected fraud activity has surpassed $600 million. Auto lending fraud may not yet have affected your portfolio, but there’s no guarantee you’re immune. Better to be prepared to block it at the front door than give it entrance and poison your portfolio.

Three of the most widespread methods of generating or providing fraudulent auto loan information are synthetic identity, fake pay stubs, and false employment verification.  

Synthetic Identity: Creating a Non-Existent Borrower

Synthetic identity combines various credentials to fabricate an identity for a non-existent person. A valid social security number combined with false personally identifiable information (PII), address, employer, and other attributes create the synthetic persona. On the surface, the profile seems legitimate, but careful verification of the individual attributes reveals subtle, even hidden, inconsistencies.

Synthetic identities are also used to falsely present an individual’s creditworthiness. Using a real identity, changes are made to the individual’s social security number and other applicant attributes. The motivation for this approach is the need to hide bad credit history. Although fraudulent, the individual sees this as the only means to obtain credit for an auto they fully intend to pay for. For lenders, though, this is a hidden risk to be avoided.

Take a few minutes to learn more about synthetic identity, the various ways identities are created, and ways to combat this type of auto lending fraud.

Faking Pay Stubs Is Easy

Need a pay stub that shows weekly and year-to-date income, deductions, and 401(k)contributions? There are dozens of websites—pay stub generators—that provide this service. All you need is a credit card or PayPal account. If you Google “fake pay stubs” you’ll get ads for:

• Make Your Own Paycheck Stub | Your Pay Stub In a Minute‎
• Create Your Fake Pay Stub Here | Easy-To-Use. No Software Need‎
• Create Check Stub in 4.99 USD | Create in 3 Simpe Step‎s

Note the incorrect spelling of “Simple” in the last example. Ironically, that’s the actual Google result. These websites guide you through the process, recommend ranges of values to enter so that your pay stub looks legitimate. For a few dollars and in a few minutes they produce a seemingly credible document for the applicant.

How prevalent is this practice? How sophisticated is the fraud that can be perpetrated? This Federal Trade Commission court filing against one of these services shows the extent of fake pay stub problem.

Proof of Employment: Call This Number

False verification of employment is just as easily arranged. For a varying fee, depending on the type of reference and level of detail required—blue collar, white collar, executive—a service provides employment verification and references. Via phone the service confirms the applicant’s employment as a direct hire, a contractor, or a consultant of one of the fictitious companies the service has established. The reference can be supported by a fake company website, email address, fax number, supervisor references, even a customized LinkedIn profile for the applicant.

Fight Auto Lending Fraud with Machine Learning and Automation

Although the internet appears to have empowered fraudsters—those who perpetrate it and those who enable it—the latest advances in machine learning and automation give lenders powerful tools to fight auto lending fraud.

When machine learning is used to analyze tens of millions of auto loan applications—legitimate and fraudulent—it learns to identify auto loan applications that are potentially fraudulent,  providing a reason and a confidence score. A lender can then apply these fine-tuned algorithms to auto loan applications at the time of arrival, and avoid granting credit to high-risk, fraudulent parties. Machine learning can recognize subtle fraud indicators that would otherwise be missed.

Automation capabilities of a modern loan origination solution further enable lenders to reduce the time and expense of dealing with fraud. When the algorithms identify a fraudulent application with a high degree of certainty and provide a reason, workflow automates the creation of the adverse action notification, populates the notification with applicant information, includes the reason for declining—incomplete or unreliable applicant information—and sends the response via email. Potentially fraudulent applications that have a low confidence score can be sent to a queue for review by underwriters to determine the validity of applicant credentials.   

If you’re facing the growing risk of auto loan fraud, invest some time to gain a better understanding of the problem and how to fight it in the The Hidden Patterns of Auto Lending Fraud Revealed webinar.

Auto Lending Fraud – Fintech

Machine learning and automation significantly reduce the risk of fraudulent applications becoming defaults and delinquencies that negatively impact portfolio profitability and performance. Savvy lenders need to fight auto lending fraud with the latest fintech capability. Without it, fraudsters have the advantage.