We’ve all seen the startling images online and on TV: brazen, smash-and-grab burglaries targeting retailers across the country. It’s no surprise these incidents catch our attention.
While not as eye-catching as these daytime burglaries, many types of financial fraud are also on the rise, while seemingly out of sight. Auto lending fraud for example—typically defined as using fraudulent information or even identities to receive auto loans—is one such area, and it has been steadily on the rise. In fact, the volume of loans using fake employer data increased a whopping 400% between 2019-2021.
When Point Predictive began tracking data in 2019, loan applications submitted to our lending partners revealed that approximately $7 million in loan value per month included fake employer data. That number spiked to an average of $35 million in the final quarter of 2021. Among its lending partners, Point Predictive has found that auto loan application fraud linked to fake employers surpassed the $1 billion mark as of December 2021, with more than 5,000 fake employers tied to fraudulent auto loan applications. And rest assured, this is only a portion of the total fake employer fraud happening in the marketplace overall.
The numbers are astounding, and fraudsters are pursuing opportunities everywhere. Fake employers provide fake pay stubs and confirm falsified incomes. These companies also create synthetic identities that can make the fraud seem even more legitimate. The fake employer can create a fake pay stub to go along with the fake identity, making the fraudsters even harder to track down.
At Point Predictive, we have seen 15 million employers included on auto loan applications across multiple lenders that participate in our consortium, which contains more than 110 million historic applications and over 10 billion unique risk attributes. The red flags start to appear when a particular employer that has never been seen in the consortium before starts to appear on many applications very quickly. Our artificial intelligence scores pick up on this pattern and alert our fraud analyst of this red flag activity. When our fraud analyst reviews the employer further it becomes obvious that the employer is fake. When a bogus company phone number or generic-looking website lead nowhere, it’s clear we have another fraudster on our hands.
While this trend is happening across the country, it’s noticeably more significant in certain states, with Texas topping the list. Between 2019-2021, the total loan value for loans using fake employer data in the Lone Star State was $201 million, which is nearly double the number two state on the list: California, where the total loan value was $115 million. Georgia, Florida and Illinois round-out the top-5. And the fraudsters don’t just target specific states, they are also attracted to certain makes and models of vehicles: Dodge Charger, Hyundai Elantra, Dodge Challenger, Nissan Altima, and Chevy Malibu are the top five automobiles for fraudulent auto loans using fake employer data.
Fraud tactics and strategies constantly change as fraudsters see what works and what doesn’t in securing a loan based on fraudulent data. That’s why dealers and lenders need to be on high alert, and we are doing all we can to help. As we’ve built the only consortium of financial institutions in the auto loan industry, we have a unique window into how auto lending fraud operates and evolves. One tool we have integrated into our fraud prevention analysis arsenal is studying and analyzing the performance of loans. For example, if a loan using fraudulent employer data gets approved, the fraudster may make one or two or zero payments on the loan before it defaults. Over time, we trace the performance of those defaulted loans and try to make connections between the employer and applicant data used to obtain the loan.
The connections and data we identify empower our lending partners to make better and more informed decisions about existing and potential fraud. Large datasets providing unique insights highlight the value of machine learning and AI, and when we analyze the auto loan data, we begin to see patterns – multiple defaulted loans where different applicants used the same fake employer data, applications associated with one employer and much more. It is through this knowledge that we gain the power to detect fraud and protect lenders, saving them millions.
We know the fake employer fraudsters don’t get the widespread attention of the smash-and-grab mobs you see on the local news. But they certainly get our attention, and when they do, we take action.