Auto lenders may face an increased risk of fraud in 2025. According to Point Predictive’s Auto Lending Fraud Trends Report there was an estimated $9.2 billion in fraud loss exposure in 2024, the highest amount ever measured.
This estimation represents a 16.5 percent increase in fraud year over year, but it is far from the only notable takeaway from the comprehensive report. Point Predictive utilized a dataset of over 250 million historical applications and $4 trillion of submitted loans to reveal several key insights about how the fraud landscape is evolving and how that is affecting auto lenders.
“This year’s report represents our largest analysis of auto lending loan data to date,” said Tim Grace, CEO of Point Predictive. “The extensive scale of our consortium and proprietary risk data provides us unparalleled visibility into fraud patterns that lenders’ own data simply cannot reveal.”
First Party Fraud Tops Risk Assessment
Even as fraud tactics become more sophisticated and high tech, the number one fraud category for 2024 was still first party fraud. It accounted for a staggering 69% of all auto lending risk. The main sources of first party fraud are split into two common categories, income and employment fraud, and synthetic identity and credit washing fraud. Income and employment misrepresentation accounted for 43 percent or $3.9 billion of total fraud risk, while synthetic identity and credit was 27 percent or $2.5 billion of fraud risk.
“While dramatic cases of organized crime ring stealing identities make headlines, our data reveals that the true story behind most auto lending fraud is an array of misrepresentations,” said Frank McKenna, Chief Innovation Officer of Point Predictive. “Borrowers using their own names who inflate their income, misrepresent their employment, utilize credit washing techniques, or create new credit profiles with Credit Profile Numbers (CPNs) account for the overwhelming majority of fraud risk, yet these patterns often go undetected.”
CPNs in particular saw an increase in usage this past year as Point Predictive’s Synthetic Identity Risk Index continues to surge, increasing 500 percent since 2017.
Credit Washing on the Rise
Credit washing saw a 162 percent increase year over year with 1.7 percent of all applications analyzed displaying some signs of credit washing fraud. This is a large jump from the 0.3 percent seen just three years ago.
The practice of filing fraudulent identity theft claims to clean up credit reports is no longer a niche strategy and appears to be gaining a real foothold alongside synthetic identity fraud. The combination of credit washing, synthetic identity fraud and identity theft accounted for 45 percent of all auto lending fraud in 2024, an increase of 41 percent year over year.
AI-Powered Fraud Emerges
Some of the more frightening threats for lenders are the ones that are still emerging in the market such as AI generators and deepfake tools. Point Predictive’s analysis of fraud focused Telegram channels saw a 644 percent increase in conversations about AI-powered fraud.
A few of the biggest emerging threats included synthetic identity generators, AI generated fake ID documents and AI enabled impersonation scams utilizing deepfake audio and video. While not as immediately widespread as the other types of fraud analyzed, use of these tools is growing fast, and auto lenders should remain vigilant as their use continues to increase.
Other Key Insights
Some more takeaways from the report include:
- Bust-out fraud increased by six percent year over year.
- True name identity theft saw an increase of 4 percent year over year, now representing $1.6 billion in fraud risk.
- Point Predictive’s Early Payment Default Risk Index has increased by 10.6 percent since early 2024.
- Systematic Dealer Risk, such as powerbooking and using fake employment for borrowers, increases lenders’ default risk by as much as 500 percent.
As fraud risk continues to rise, it is important for lenders to stay vigilant against threats and knowledgeable about how the fraud landscape is changing.
“This data enables us to provide solutions that assist lenders in funding loans that do not contain fraud and misrepresentation while avoiding the losses associated with the array of hidden schemes that can lead to defaulted loans,” said Grace.
