Same Exact Cars, Different Prices: FTC, Lawmakers Express Concerns Over AI-Driven ‘Surveillance Pricing’

WASHINGTON — Increasing the likelihood members and consumers will pay different prices for the same cars, the growing use of artificial intelligence to set individualized prices based on consumers’ personal information is drawing scrutiny from federal regulators and lawmakers, including concerns about what the practice could mean when Americans shop for cars — and for credit unions that finance those purchases.

The practice, often called “surveillance pricing” or “personalized pricing,” uses algorithms and consumer data to estimate what an individual is willing to pay and potentially offer different consumers different prices for the same product or service.

The Federal Trade Commission has found that companies offering algorithmic pricing technology can use detailed information including consumers’ location, demographics, browsing behavior and shopping history to tailor prices and promotions.

The FTC has specifically identified automobiles as an area where surveillance pricing could pose heightened concerns because consumers may invest considerable time in the purchasing process before learning the vehicle’s actual cost.

There is no clear evidence, however, that major used-car retailers are currently taking an individual shopper’s income and using AI to change the advertised price of the same vehicle. Instead, regulators are raising concerns about technology that has the capability to use personal information — including data that could reveal or help infer a consumer’s financial circumstances — to determine willingness to pay.

AI Can Build a Consumer Profile

When the FTC launched an investigation into surveillance pricing in 2024, the agency said companies were offering technology that combined advanced algorithms and AI with information including location, demographics, credit history and browsing or shopping history to categorize consumers and establish targeted prices.

The FTC subsequently found pricing intermediaries could access direct and inferred consumer data and use that information to influence the prices consumers see. Even mouse movements, abandoned online shopping carts and previous searches can become inputs into pricing systems, according to the agency.

That means an algorithm would not necessarily need to know that a car buyer earns, for example, $150,000 annually.

Instead, data including ZIP code, credit characteristics, purchasing patterns, browsing behavior and other demographic and behavioral information could potentially allow an algorithm to infer the consumer’s economic circumstances or willingness to pay.

The FTC said advances in machine learning have made collecting and analyzing large amounts of such information significantly easier and warned consumers could potentially encounter surveillance pricing when purchasing anything from groceries to a house or car.

FTC Considering New Pricing Policy

The issue has taken on new urgency.

The FTC in August proposed an enforcement policy addressing personalized pricing, which it defines as using personal data to establish prices according to what a company believes an individual consumer is willing to spend.

FTC Chairman Andrew Ferguson said consumers seeing a listed price generally expect other consumers to see the same price rather than a retailer’s estimate of how much they personally are willing to pay.

The commission said it does not have authority to prohibit personalized pricing in all circumstances but warned that businesses failing to disclose how personal information is being used to establish prices could violate the FTC Act or other laws.

The FTC is accepting public comments on the proposal through Sept. 18.

Congress also is investigating the issue.

House Oversight Committee Chairman James Comer, R-Ky., said Wednesday the committee is continuing an investigation begun in March into the use of AI for surveillance pricing.

Comer said technology exists to track consumer behavioral information and build profiles using geolocation, demographics, browsing and purchase histories, device type, battery life and even mouse clicks.