On April 28, 2026, Governor Wes Moore of Maryland has signed into law the nation’s most aggressive state law aimed at so-called “surveillance pricing” and algorithmic price-setting. House Bill 895, titled the Protection From Predatory Pricing Act, will become effective on October 1, 2026. It restricts the use of personalized pricing, consumer data-driven pricing, and certain AI-enabled pricing practices, particularly in the food retail (operating establishments of at least 15,000 square feet) and delivery sectors. Although the statute expressly excludes financial institutions from portions of the law, it should be viewed as an important signal of where state lawmakers may be headed next.
While this legislation deals only with grocery stores of a certain size and third-party food delivery apps, it is an early indication that state regulators and legislatures are increasingly skeptical of individualized pricing models powered by consumer data and artificial intelligence. If that trend continues, other types of businesses, consumer financial services, may become future targets.
What the Maryland Law Does
The statute addresses two related concerns:
1. Restrictions on Dynamic Pricing for Food Retailers
The law prohibits certain food retailers and third-party food delivery platforms from using “dynamic pricing” to charge higher prices for tax-exempt food sold to specific consumers. The law broadly defines dynamic pricing as personalized pricing based on a consumer’s personal data, including pricing determined through AI systems or models that retrain or recalibrate in near real time.
The statute also bars those businesses from using surveillance personal data to charge higher prices to an individual or group of consumers.
At the same time, the law recognizes several exceptions, including:
- loyalty and rewards programs
- promotional discounts
- subscription pricing
- publicly disclosed group discounts (students, seniors, veterans, etc.)
- pricing based on geography, taxes, shipping, supply constraints, or inventory shortages
- voluntary data-for-discount arrangements
- general price changes based on supply and demand that do not use individualized consumer data
2. Disclosure Requirements for Other Merchants
For merchants outside the food-retail category, the law requires clear disclosures when prices are set using algorithms or personal data. Specifically, a merchant that uses dynamic pricing or personal data to set prices and then advertises those prices must disclose:
“THIS PRICE WAS SET BY AN ALGORITHM OR BY USING YOUR PERSONAL DATA.”
Why This Matters for Consumer Financial Services
The law excludes financial institutions and GLBA-covered entities from one section of the statute. But exemptions today do not guarantee exemptions tomorrow.
Indeed, many consumer financial products already rely on individualized pricing models that use data analytics and machine learning, including:
- credit card APR offers
- personal loan rates
- auto finance pricing
- mortgage pricing adjustments
- insurance-adjacent financial products
- overdraft alternatives and small-dollar credit
- targeted fee waivers or incentives
- marketing offers personalized through behavioral data
Traditionally, such pricing has been defended as risk-based pricing, which has long been accepted when tied to legitimate underwriting factors. But policymakers increasingly may ask a different question:
When does legitimate risk-based pricing become impermissible surveillance pricing?
That question becomes even sharper when models incorporate nontraditional data, geolocation, browsing behavior, device information, purchase history, or inferred behavioral traits.
Expect Fair Lending Theories to Expand
Maryland’s law also prohibits the use of “protected class data” where it has the effect of denying accommodations or privileges available to others. That language reflects a familiar anti-discrimination framework.
Consumer financial services companies should expect similar arguments under:
- The Equal Credit Opportunity Act
- The Fair Housing Act
- State Unfair, Deceptive, or Abusive Acts or Practices (UDAAP) theories
- State mini-CFPB laws
- State privacy statutes regulating profiling and automated decision-making
Even where protected-class variables are not directly used, state regulators may challenge proxy discrimination, disparate impact, or opaque algorithmic outcomes.
The Coming Debate: Personalization vs. Fairness
Supporters of individualized pricing argue it can:
- improve efficiency
- match prices to risk
- expand access to credit
- offer discounts to price-sensitive consumers
- reduce losses and lower overall costs
Critics argue it can:
- penalize vulnerable consumers
- exploit urgency or desperation
- hide discriminatory outcomes
- undermine price transparency
- create a marketplace where every consumer pays something different
That debate is no longer theoretical. Maryland has now legislated in this area.
What Financial Institutions Should Do Now
Even though the law currently carves out many financial entities, banks, fintechs, lenders, and servicers should not ignore it. They should begin evaluating:
- what data influences pricing decisions
- whether pricing models use behavioral or surveillance-like inputs
- how explainable their models are
- whether disparate impacts can be measured and mitigated
- whether consumer disclosures about personalization are adequate
- whether state legislatures could target their sector next
Final Thoughts
Maryland’s new law may be remembered as one of the first significant legislative attacks on surveillance pricing. While the immediate focus is food retail and merchant pricing, the broader principle is clear: lawmakers are increasingly uncomfortable with opaque AI systems using personal data to determine what each consumer pays.
Consumer financial services has historically relied on personalized pricing more than almost any other industry. That reality makes the sector a likely future battleground.
Today it is grocery prices. Tomorrow it could be loan pricing, fees, interest rates, or credit offers. Institutions that assume this debate will stop at the supermarket door may be making a serious mistake.