Auto dealers and lenders are living through one of the most turbulent stretches in the industry’s history. Fraud losses in auto lending now run 21 times higher than losses in credit cards and six times higher than losses in unsecured personal loans, according to TransUnion analysis presented at the Auto Finance Summit.
Average dollar losses per fraudulent auto loan sit just under 20,000 dollars, well above other consumer loan categories, and losses are increasingly concentrated among borrowers in credit tiers traditionally viewed as lower risk. Generative AI tools are compounding the problem, with fraudsters producing synthetic pay stubs, deepfake identity documents and AI generated disputes designed to strip accurate negative information from credit files.
The strain shows up on the dealer floor too. Nearly 90% of dealers say they are concerned about rising fraud, and 75% report it is already having a measurable impact on their operations, according to Experian’s latest State of the Automotive Finance Market survey. Separately, ninety-day auto loan delinquencies climbed to 5.60% in the first quarter of the year according to the Federal Reserve Bank of New York Q1 2026 Report on Household Debt and Credit, reflecting numbers not seen since 2010, in the aftermath of the Great Recession. Synthetic identity related loans carry delinquency rates three to five times higher than legitimate loans, according to Equifax’s Auto Insights report.
It is against this backdrop that federal lawmakers have reentered the conversation about how artificial intelligence should be governed, and what that governance could mean for industries, like auto finance, that depend heavily on AI powered underwriting and verification.
Introducing the Great American AI Act
On June 4, 2026, Representatives Jay Obernolte (R-CA) and Lori Trahan (D-MA) released a 269-page discussion draft known as the Great American AI Act. The bill has not yet been formally introduced, and it is the product of the bipartisan House AI Task Force, on which both lawmakers served. The draft is organized into four titles covering frontier AI governance, workforce impact, cybersecurity, and research and international cooperation.
Three provisions in Title I are especially relevant for lenders. First, large developers of frontier AI models would be required to publish a framework describing how they identify and mitigate catastrophic risk, along with regular transparency reports. Second, those same developers would be required to retain independent verification organizations (IVOs) to audit their models twice a year and report findings to a newly created federal office called the Center for Artificial Intelligence Standards and Innovation. Third, the bill would preempt state laws that regulate AI development directly, for a period of three years.
The bill also sets a firm flag in the ground to combat the emerging threat of AI-generated fraud. Specifically, it incorporates the bipartisan AI Fraud Deterrence Act, which amends existing federal fraud and money laundering statutes to increase maximum fines from $1 million to $2 million and increase penalties when AI is used. For an industry currently battling AI-generated fraud at the point of loan application, this inclusion is a clear message from Capitol Hill: regulators view AI-enabled fraud as an immediate crisis, and they are building a punitive enforcement framework to match the threat.
The bill also includes a whistleblower protection provision shielding employees and contractors who report AI related violations from retaliation, and it incorporates several previously introduced bipartisan measures addressing cybersecurity information sharing and AI workforce data collection.
Why This Matters Beyond Silicon Valley
At first glance, a bill focused on frontier AI developers might seem distant from day-to-day lending operations. But the logic behind the legislation, that AI systems shaping consequential decisions deserve independent scrutiny, is already showing up closer to home. State legislatures in California, New York and Illinois have each passed their own frontier model laws in the past year, and Illinois already requires third party audits similar to the IVO structure proposed federally. Auto lenders that rely on AI for underwriting, income verification or fraud detection are operating in an environment where independent audit and verification expectations are becoming more, not less, common.
The preemption provisions add a further wrinkle. If enacted as drafted, the federal preemption would apply only to development stage activity and would not touch state rules of general applicability and law regulating AI use or deployment meaning many consumer facing state protections would remain intact. Dealers and lenders should not assume that a federal framework, if it eventually passes, would replace the patchwork of state requirements they already navigate.
A Long Runway, Not a Deadline
It is worth remembering that the Great American AI Act is a discussion draft, not enacted law. Complex legislation of this scope typically takes years to move through committee, floor votes and reconciliation between chambers, and this draft has already drawn opposition from the House Democratic Commission on AI even as other members signed on in support. History suggests that today’s draft language often previews the shape of future laws and regulation well before those rules take effect.
That is precisely why the current moment matters. The provisions in the bill, model transparency, independent audits, workforce impact reporting and preemption with a built-in sunset, reflecting the direction of federal thinking. Lenders who wait for a final law before examining their own AI governance risk being caught flat footed when state or federal requirements eventually catch up with the technology they already use.
Getting Ready Now
For dealers and auto finance leaders, the most useful response to this legislative moment is not to predict its outcome but to prepare for its direction. That means documenting how AI models used in underwriting and fraud detection are validated, understanding whether current vendors could support an independent audit if one were required, and building internal processes that can adapt to a regulatory landscape that is likely to keep shifting between state and federal authority for years to come. Auto finance has weathered regulatory uncertainty before. The lenders best positioned for what comes next will be the ones treating AI governance as an operational discipline today, rather than a compliance requirement to address once the rules are finalized.
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