Auto loan fraud reached $9.2 billion in 2024—the highest ever recorded—representing a 16.5% increase year-over-year. Early payment defaults (EPDs) climbed 25% in the past 24 months. But here’s the critical insight: up to 70% of early payment defaults contain evidence of fraud on the initial application. The dangerous intersection where fraudulent loan origination meets strategic default behavior creates what the industry calls “intentional skips.”
What is an “intentional skip?”
An “intentional skip” refers to a borrower deliberately defaulting on a loan payment and then trying to hide their location to avoid the lender. This is different from a lender-approved skip payment, which is a temporary, agreed-upon pause in payments.
The scale of the problem extends far beyond subprime portfolios. Auto loan fraud losses are 21 times higher than credit card fraud losses, and among super-prime borrowers, average fraud losses reach $53,796 per incident.
Understanding the two distinct paths to skips is critical: Some borrowers never intended to repay from the beginning, using fraud to secure loans they always planned to abandon. Others entered the loan with genuine intent but faced financial circumstances that made continued payment impossible.
This post examines financial hardship-driven skips and fraud-enabled skips, providing lenders and financial institutions with frameworks to distinguish between them and strategies to address each.
A significant amount of fraud arises from the financial hardship borrowers face when they intended to repay but were forced into economic stress that have trouble paying. Knowing the distinction between the two is critical because hardship skips can respond to assistance-based intervention, while fraud-enabled skips will not.
Subprime auto loan delinquencies reached record levels in 2024-2025, with over 6% of subprime borrowers at least 60 days past due. Prime borrower delinquencies also climbed to 15-year highs, signaling affordability challenges rather than planned fraud:
Financial stress typically shows a simultaneous decline in performance across all credit products. When a borrower loses their job, has a medical emergency, or faces financial hardship, credit card payments become delinquent alongside their auto loan, mortgage, rent, and other bills.
Loan-to-Value (LTV) ratios of 120 to 125% are often associated with rapid vehicle depreciation, especially on longer loan terms like 72 or 84 months. A borrower who financed $40,000 on a vehicle now worth $30,000 owes $10,000 more than the value of the collateral.
Borrowers experiencing financial hardship across multiple obligations see continuing payments on an underwater asset while falling behind on rent, utilities, and food expenses as economically and emotionally irresponsible. The hardship borrower wants to keep paying, but genuinely cannot, unlike the fraud-enabled skipper, who calculates whether continuing to pay serves their strategic interest.
Financial hardship exhibits specific patterns distinguishing it from fraud:
First-party fraud, in which borrowers or dealers misrepresent information, dominates the auto lending fraud landscape, accounting for 69% of the $9.2 billion in fraud risk exposure. Unlike financial hardship skips, where borrowers intended to repay, fraud-enabled skips often begin with some level of deception at origination. The borrower may have partial, complete, or opportunistic intent to default depending on how circumstances unfold.
Income and Employment Misrepresentation (43% of total fraud risk, $3.9 billion)
Borrowers overstate earnings to meet Payment-to-Income (PTI) requirements, qualifying for loans they cannot actually afford. If a lender requires a PTI of 15% or less, a borrower earning $3,000 per month can only qualify for a $450 car payment. But if the borrower inflates income to $5,000, they suddenly qualify for a $750 monthly payment, providing access to a more expensive vehicle than their actual financial situation warrants.
Employment facilitation coupled with income inflation. Borrowers list false employers, exaggerate job titles, and may use credit repair companies to verify employment for a fee. In 2022, over 10,000 false employers were linked to over $3.1B in fraudulent loan applications. Fake employers maintain phone numbers where accomplices answer verification calls, confirm employment and income, and provide fabricated paystubs.
Credit Washing (a 162% year-over-year increase) involves the fraudulent dispute of legitimate negative tradelines, usually under the guise of identity theft, to boost credit scores temporarily. Credit washing indicators were found in 1.7% of auto loan applications in 2024, up from 0.3% three years earlier. Because the Fair Credit Reporting Act requires credit bureaus to investigate disputes within 30 days, filings can overwhelm them, leading some to temporarily remove legitimate negative tradelines while investigations proceed, artificially boosting scores by 50-100 points.
Synthetic Identity Fraud (up 500% since 2017). Borrowers create “enhanced” versions of themselves using Credit Privacy Numbers (CPNs), which are essentially stolen or fabricated Social Security numbers combined with their real names. The goal is to access credit that would otherwise be unavailable or to secure better terms than their credit history warrants.
Not all application fraud indicates identical skip risk:
The presence of origination fraud significantly increases the risk of early-payment default. Point Predictive’s Early Payment Default Risk Index has risen by 25% over the past 24 months, with EPD rates now more than double the 2017 baseline—borrowers who inflated their income or washed their poor credit are already demonstrating comfort with deception.
For borrowers who committed fraud at origination, specific economic and behavioral triggers transform those fraudulent loans into intentional skips.
When loan-to-value ratios exceed 120-125% due to rapid vehicle depreciation, borrowers with fraudulent applications have a significantly higher possibility to default. Unlike hardship borrowers, fraud-willing borrowers calculate the pure economics of vehicle value relative to the amount owed.
Credit washing is specifically timed to create temporary credit score boosts for refinancing attempts:
Credit Washing differs from hardship-driven refinancing attempts because the fraud-enabled borrower is only attempting to capitalize on an artificially inflated score, abandoning the loan entirely when the scheme fails.
Unlike organized bust-out fraud, in which fraudsters never intend to repay, the borrower makes 6-18 months of payments, establishing plausible deniability. Then, the borrower strategically defaults when the vehicle’s value declines or their financial circumstances change.
The vehicle is often sold before repossession, maximizing the fraudster’s benefit while leaving lenders with significant losses.
Fraud-enabled skips show specific patterns that distinguish them from hardship-driven defaults:
Traditional analytics struggle to distinguish between fraud-enabled skips and hardship-driven defaults because of fundamental structural disconnects.
An application flagged for minor income inconsistency but ultimately funded may be classified as a “false positive fraud alert.” Six months later, when that account goes into early payment default, the collections team sees only payment history, not the origination flags. The connection between origination fraud and strategic skip goes unmade.
The inability to distinguish between fraud and legitimate hardship means lenders deploy the wrong intervention strategies: offering payment deferrals to fraudsters who will never be cured, or aggressively pursuing collections against hardship borrowers who would respond to assistance.
Distinguishing fraud-enabled from hardship-driven skips requires analyzing data across multiple systems:
The temporal dimension is critical. Static snapshots miss evolutionary patterns. How payment behavior changes over time relative to origination characteristics reveals intent, and how credit profiles evolved before application reveals manipulation.
The challenge for auto lenders is that using traditional credit scoring techniques and loan decisioning guidelines will not identify the early indicators of potential problems. Lenders and credit unions must move beyond analyzing static data points from a limited number of data sources to a more comprehensive approach that combines data from credit bureaus, in-house data, employment history, and other third-party, permission-based sources to identify signals – early indicators of a borrower that might be at risk or that an intentional fraud is being committed.
Using a platform like dotData’s Feature Factory, a lending Data Science team could combine data from applications, tradelines, payment data, and even permissioned third-party data like bank accounts to identify several critical signals indicative of hardship-related stress:
For risk managers, business analysts, and other decision makers who need to understand and act on potential problem borrowers, a product like dotData Insight – individually or coupled with Feature Factory – can provide an intuitive, point-and-click approach to identifying critical micro-segments to act upon:
Driver 1: Average Percentage of Delinquencies Over 60 Days in tradelines in the last 2 years is more than 33%
Driver 2: Percent of records with Narrative Codes ‘Charged Off Account’ in tradelines in the last 5 years is between 15% and 100%
Combined Hardship Skip Micro-Segment: When combined, these create a profile of someone with a documented five-year history of defaulting on accounts that have been charged off, who is also currently severely delinquent across multiple credit products. A pattern highly characteristic of an individual overwhelmed by debt, sliding into hardship, and skipping.
Intervention Strategy: This 1.33% segment has a 25.9% higher skip rate. Early intervention can salvage relationships and reduce charge-offs. These borrowers are in genuine distress and likely to respond positively to assistance-based approaches.
Minimizing the impact of fraud-enabled skips means identifying them early, preferably during origination. By leveraging the power of multi-source, multi-table AI-driven analysis, data scientists can spot the subtle signal differences from hardship-related skips that help flag potential fraud threats early:
Like we did earlier for hardship-related skips, risk managers, analysts, and other subject matter experts can leverage the power of tools like dotData Insight to combine signals into powerful micro-segments:
Driver 1: Number of records in public records with ECOA Code ‘J’ in the last 30 days is 19 or more
Driver 2: Percent of records with Narrative Codes ‘Disputes Account Information’ in tradelines in the last 3 years is between 4.2% and 100%
Combined Fraud-Skip Micro-Segment: When you see anomalous public records combined with high velocity of disputes, the profile is not one of a person falling behind on debts, but someone actively manipulating their credit file to appear more creditworthy than they are.
Intervention Strategy: This 0.6% segment—though small—has a skip rate 23.5 percentage points higher than the portfolio average. These accounts warrant:
Addressing both fraud-enabled and hardship-driven skips requires coordinated strategies with fundamentally different approaches for each.
Enhanced verification for applications showing fraud risk factors:
The critical balance is in reducing fraud without creating excessive friction for legitimate borrowers. Risk-based identity verification applies intensive checks only to applications flagged by predictive models, optimizing the trade-off.
Separate collections queues for fraud-skip risk versus financial hardship defaults enable specialized approaches.
Best-in-class auto lenders using behavioral segmentation and advanced analytics to distinguish fraud from hardship achieve 10-15% reductions in net charge-offs. Given that auto loan charge-offs amount to billions in annual losses, even modest improvements translate into substantial savings.
Early detection of skip type is the best defense. Average auto fraud loss per incident is approximately $19,600, but losses exceed $53,000 for super-prime borrowers. Early detection and appropriate intervention can reduce fraud losses by 40-60% while salvaging hardship accounts that would otherwise be charged off.
Financial hardship indicates deteriorating performance across all credit products simultaneously, with the borrower’s entire financial life collapsing. Consumers in this category respond to lender outreach, express willingness to work out solutions, and try to keep the vehicle.
Fraud-enabled skips show selective default. Unsecured tradelines, like credit cards, are current, while the secured ones, like auto loans, are abandoned. The inverted priority shows a clear intent rather than an inability to pay. Fraud-enabled skippers avoid communication, may relocate the vehicle, and won’t care about hardship program offers.
The next frontier in auto lending risk management is connecting the dots between origination characteristics and downstream behavioral patterns, detecting intentional skips early, and deploying the right intervention strategy based on whether the root cause is fraud or financial hardship. Those who master this distinction will gain a competitive advantage through reduced fraud losses.
Here are 5 FAQs from the article, designed to be short, compelling, and well-defined for SEO purposes:
Intentional skips occur when borrowers misrepresent information during the loan application and later strategically abandon their auto loans, often when economic incentives shift.
Auto loan fraud reached a record $9.2 billion in 2024, with early payment defaults climbing 25% in 24 months, and up to 70% of these defaults show evidence of fraud at application.
Key fraud tactics include income inflation, employment fabrication, credit washing (artificially boosting credit scores), and creating fictitious identities using both real and fake information.
Traditional systems often operate in silos, focusing on origination fraud or payment behavior separately. This creates a gap, preventing the connection of initial fraud indicators with later strategic default patterns.
AI-driven feature discovery automatically uncovers non-obvious combinations of origination fraud signals and behavioral changes throughout the loan lifecycle, allowing lenders to differentiate between economic hardship and intentional skip behavior for targeted interventions.
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