The following post is provided by Informed.IQ, a MeridianLink® Marketplace partner.
By Jessica Gonzalez, VP of Customer Success, Informed.IQ
Fraud does not always announce itself within the auto lending platform at the time of origination.
Sometimes it surfaces later, through charge-offs, early payment defaults, and losses that suddenly hit the P&L. By the time those indicators appear, the exposure is already booked.
Over the past 180 days, a small subset of identified bust-out activity drove an estimated $11.8 million in losses in the auto finance ecosystem. Based on this exposure, Informed estimates the entire ecosystem is experiencing a double-digit acceleration in bust-out activity, broadly consistent with a 10–15% year-over-year increase tied to rising early-payment-default and credit-velocity risk. These outcomes were not tied to a single failed control or an isolated misrepresentation. They emerged from repeatable patterns observed across identity reuse, accelerated credit behavior, vehicle selection, dealer concentration, and tightly clustered timing across multiple lenders.
That distinction matters. Because this risk did not come from what lenders failed to verify within their auto lending platform. It came from what they could not see.
The pattern was not an anomaly
In a recent review of credit stacking and bust-out alerts, provisioned by Informed, we examined a subset of funded auto loans exhibiting rapid, multi-lender vehicle acquisition behavior.
What emerged was not an edge case. It was a repeatable pattern.
Applicants executed multiple signed retail installment sales contracts across different lenders and vehicles within days, not months. In some cases, activity occurred within forty-eight hours. In others, within two to three weeks. Each transaction, reviewed independently, appeared explainable. Viewed together, the risk was unmistakable.
- Three to six vehicles
- Three to five lenders
- Days, not quarters
Importantly, the vehicles associated with this activity were not anomalous. They aligned with vehicle types commonly tied to fraud and theft, including the Chevrolet Silverado. In this review, Silverado transactions were most frequently associated with identified bust-out activity, with the highest concentration occurring in Texas and California. These are markets where underwriters are routinely instructed to apply heightened scrutiny. Yet these loans were still approved and funded. This highlights a critical limitation of legacy fraud detection within auto lending platforms: simple queries and heuristic reviews based on known risk vehicles or theft trends are no longer sufficient to identify coordinated, velocity-driven bust-out behavior.
What one lender sees, the network sees differently
This activity did not surface through standard verification workflows.
It emerged through monitoring designed to identify credit stacking and potential commercial usage risk across a shared fraud network. What one lender sees as a single deal, the network sees as behavior.
The exposure is material.
In one illustrative example, a small cluster of funded loans generated more than $250,000 in potential loss for a single institution in a single month. At the current pace, that exposure scales to more than $4 million in annualized bust-out risk if left unmitigated. Notably, this figure reflects only post-funding visibility and does not account for downstream impact from early payment defaults, charge-offs, or the operational cost required to investigate and recover losses.
Bust-out fraud is defined by compounding signals
Bust-out fraud is not defined by one red flag that pops up in your auto lending platform. It is defined by signals that compound, adapt, and intensify when viewed across the full lending ecosystem.
When analyzed in isolation, each data point may appear rational. When analyzed in aggregate, the risk becomes systemic.
In several cases, applicants executed five or more RISCs across multiple lenders and vehicles within fewer than twenty days. In others, six vehicles were acquired across four lenders in under a week. In multiple instances, transactions spanned several lenders within forty-eight hours.
These are early indicators of behavior that historically correlates with elevated first payment default and early charge-off risk.
Velocity changes the risk equation
Some scenarios may resemble legitimate commercial use cases, such as rental or fleet activity. But legitimacy is not determined by intent alone.
Velocity matters.
Concentration matters.
Timing matters.
When multiple vehicles are acquired simultaneously across lenders, risk escalates regardless of stated purpose. This is where many fraud programs remain misaligned.
Why traditional controls miss it
Most controls within auto lending platforms are designed to validate accuracy at the transaction level. Documents are checked. Data points are confirmed. Consistency is evaluated at the moment of underwriting.
Credit stacking does not rely on falsified documents. It relies on speed. By the time traditional controls surface concern, exposure is already booked.
In this review, all identified cases were already funded. Additional analysis at credit or underwriting could have provided further insight, but the opportunity had passed. The signal existed. The visibility was not automated.
Automation in the fraud network is the difference
Auto lenders cannot rely on periodic reviews to catch patterns that unfold in days. That approach is not scalable, repeatable, or sustainable. They need continuous, network-level monitoring within their auto lending platform that surfaces behavior as it happens, not after losses materialize.
Credit stacking is not new. What is new is the velocity at which it is occurring and the ease with which it bypasses lender-specific controls.
Fraud today is not quieter. It is faster and it is connected.
The institutions that win will not be the ones that add more manual review. They will be the ones that recognize fraud as a network problem and respond with network-level visibility.
Because the next loss will not come from what you failed to verify. It will come from what you could not see.
To learn more, visit informediq.com or Informed.IQ on Linkedin.
