VIEWPOINT: Auto finance's first line of defense

There is a growing issue in US auto finance originations around fraud and subsequent potential credit delinquencies. Even as I write this, I note a new report indicating that credit delinquencies on securitized nonprime auto loans have reached their highest level in seven years. Following recent high-profile fraud cases and subsequent losses for funders in the ABS markets, investors will surely seek more demonstrable controls to prevent fraud. 

The escalating scale of auto loan fraud

Recent statistics do not make for pleasant reading:

• Point Predictive’s 2025 Auto Lending Fraud Trends Report states that fraud exposure in auto loans hit $9.2bn in 2024. This was the highest on record, driven largely by misrepresentation of borrower characteristics, the growing use of synthetic identities, and errors in income reporting and verification.
• TransUnion data shows that, in fact, fraud losses in auto lending are 21 times higher than credit card fraud losses, and six times higher than unsecured personal loan losses - even though auto fraud incidence is often lower.
• Early payment default risk, where customers stop paying their loans within the first six months, is seen as a key early warning for fraud or misrepresentation. According to Point Predictive’s analysis, approximately 70% of these cases indicate evidence of fraud in the original application data upon review.

It is therefore vitally important for finance companies in the auto space to use all the tools at their disposal effectively, to be able to pick out the weeds; the fraud and misrepresentation that can cause such pain to their businesses, and take up so much of their operational teams’ time to identify. They need to focus on the quality of the data flowing through their systems, not just the speed at which they process it.

Shifting detection to pre-decisioning

One of the major benefits for auto lenders integrating RouteOne and other dealer portals into a state-of-the-art originations system is the ability to use the combined power of both platforms. Such an integration enables the creation of a validated data journey, shifting fraud detection from post-funding to pre-decisioning.

Structured data fields provided by dealers are routed through a vast network of explainable decisioning rules, enabling immediate rejections or requests for rework based on specific fields or field combinations. By preventing problematic transactions, this process saves time for both dealers and funders. Simultaneously, the errant information can be used constructively; it can be stored in the finance platform database for reporting, KPI tracking, and rule iteration in future fraud monitoring.

The first line of defense

Payments Journal reports that the most frequent red flag involved issues with the applicant’s phone, such as an area code that does not match other details provided. Alongside other similar concerns, such as the use of certain email domains, this issue can be easily detected by applying business rules to structured data from a dealer portal like RouteOne, before the application proceeds to underwriting. 

Despite the growing ease of using AI to generate realistic but fake documents, the most common frauds still rely on stolen rather than synthetic data, because it requires much less effort for the fraudster. Generally, according to Payment Journal, they use their own license with a stolen Social Security number. This means that validation across fields using the complex decision framework on the dealer structured data can likely identify this before it reaches the lender's automated underwriting process, making it a significant advantage for finance companies as a first line of defense.

Connecting the dots through data lineage

Data stored in the finance system, both on live loan contracts and previous credit applications, whether approved or rejected, can also be fed into this business rules engine to prevent other, more coordinated fraudster tactics. These could include multiple applications by ‘mules’ using stolen identities across different dealerships, or the use of manufactured transaction histories based on those with previous credit approvals obtained through credit-builder companies.

This highlights the need not only for direct integrations with dealer systems and a powerful business rule engine within the finance platform, but also for built-in, easily accessible, traceable data storage with clear lineage between your originations and servicing systems. This is most obviously and robustly implemented by having a single system performing both functions. By combining data on potential misrepresentation and previously qualified individuals, companies can analyze and deploy these insights to better mitigate this growing risk.

Transparency and integrity in the dealer network

Dealers are busy, and they operate in a competitive market. The up-front validation not only helps them identify potential fraud but also provides a direct, quick, and comprehensible response, allowing them to request further verification and clarify uncertainties while the customer is in front of them. This is a world away from sending an application into a vacuum and receiving a response from a black box. It is a systematic, structured, rules-based response that allows resolution, learning, and iterative processes.

Mistakes or misrepresentations do not just affect credit approval; they can also be used to access special programs or subvented rates, and this is a growing area of auto finance fraud. Interestingly, these could be led by the customer or the dealer, who could be incentivized to secure the ‘best deal’ for their customer. This could take the form of the above misrepresentation, but could also include incorrect information on residency, vehicle details (used/new/mileage), or whether they are existing customers accessing loyalty programs. Triggered pricing rules or promotions can be validated against the customer profile, and real-time cross-validation of profile fields against one another or external data sources on application entry could highlight discrepancies and anomalies.

This makes it just that little bit more difficult, and helps the finance company maintain its KPIs.

Building resilience with cleaner originations data

These benefits can all be achieved through cleaner originations data: structured inputs, intelligent, systematic validation and cross-validation, and the use of stored data throughout the lifecycle. And this does not yet incorporate the other features that can be included in the originations process - either through the dealer portal or in the finance company’s platform - to validate document contents automatically, massively speeding up the process. This, though, will be more about speed and efficiency than about preventing the most common frauds, as I suspect it would be quite a foolish fraudster who submits incorrect identity documents on the off-chance they are not checked. 

The landscape of fraud is constantly shifting. As bad actors' tactics evolve, so must our defenses. The more they try to exploit the system, the more robust the barriers we must put in place.

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