Fraud Losses Reach Alarming Levels as Synthetic Identities Exploit Lending Systems
Auto lenders generally assess risk based on credit scores, positing that higher scores indicate lower potential for fraud. However, a recent report from TransUnion reveals a concerning trend: Superprime borrowers—those with credit scores over 720—are generating three times more fraud losses than their subprime counterparts.
This significant discrepancy is attributed to the rise of synthetic identities, which fraudsters exploit to navigate weaknesses in lending policies. The latest auto lending fraud report from TransUnion indicates that, in 2023, the average financial loss for superprime auto loans reached $53,796, starkly contrasted with $16,015 for subprime loans.
Fraud prevention strategies typically focus on subprime applicants, leaving prime and superprime borrowers with less scrutiny. TransUnion’s findings illustrate a marked increase in the use of synthetic identities by fraudsters masquerading as ideal borrowers, significantly enhancing their potential for greater financial gains.
“Fraudsters often perpetrate bust-out schemes using synthetic identities, allowing them to acquire multiple vehicles in quick succession,” said Frank McKenna, chief fraud strategist at PointPredictive. This sophisticated approach elevates average losses substantially, as these identities can secure up to ten vehicles within just 30 days.
The traditional model of risk assessment appears to have created an “inverted risk pyramid” for lenders, prioritizing their most advanced fraud detection tools toward lower-value transactions while allowing high-value loans to bypass thorough checks. The economic rationale is straightforward: a single loan for a $60,000 luxury car offers considerable returns compared to numerous smaller credit applications.
Fraud tactics have also become more organized, with online forums where fraudsters claim to generate multiple synthetic identities each week, which they subsequently ‘age’ to enhance their legitimacy. This has led to a surge in the usage of high-limit primary tradelines that further boost credit scores, complicating detection efforts for lenders. These unauthorized tradelines provide a more robust scoring effect compared to legitimate ones.
As fraud detection models are primarily designed to identify credit risks, they often remain blind to deceptive practices. For instance, a potential borrower in their mid-30s boasting a high credit score but with a relatively young tradeline may not raise any alarms. Red flags such as sudden high-limit tradelines, temporary communication channels lacking historical context, and minimal social media presence are often overlooked.
The ongoing transition of auto lending toward digital platforms exacerbates these vulnerabilities, as the critical in-person identity verification methods become less prevalent. According to a representative from TransUnion, the lack of physical validation increases the risks associated with synthetic identity fraud.
To mitigate these risks, lenders are encouraged to adopt enhanced fraud detection measures that encompass additional behavioral attributes and cross-verify identity claims during the application process. Implementing a layered approach to fraud detection can better equip lenders to identify high-risk applications, particularly those presenting as superprime, and facilitate proactive monitoring of accounts.
