Many of the tens of millions of Americans shopping for deals today have decided to spend more than they can afford. According to the National Retail Federation, holiday spending is projected to top $1 trillion for the first time, with the average person planning to spend about $890.

As many as 66 percent of Americans expect to borrow for holiday shopping, and about a quarter anticipate taking on $900 or more in holiday debt. Many holiday borrowers will carry that debt for months on credit cards and buy now/pay later plans—and, for the most vulnerable, payday loans. 

For payday lenders, this holiday season promises to be different: many have deployed AI to speed up approvals and improve targeting. And some of their customers have had their precarious finances damaged by the longest government shutdown in U.S. history.

Common Knowledge 

Payday loans have long been marketed as quick fixes for financial emergencies. In reality, they are among the most expensive forms of credit. The typical payday loan carries an APR around 300 to 400 percent—for example, a $100 loan with a $15 fee for two weeks equates to nearly 391% APR. In some permissive states, average rates exceed 600 percent; Texas has been reported around 664 percent. While a number of states cap interest at 36 percent, many still allow triple-digit APRs.  

Roughly 12 million Americans take out payday loans each year, or about 4 to 5 percent of adults. Annual volume exceeds 20 million loans, totaling $8.6 billion in principal and $2.4 billion in fees. Defaults are common: about 1 in 5 borrowers ultimately default on a payday loan, according to federal research, and so-called “rent-a-bank” high-cost lenders have reported loss rates averaging around 50 percent in public filings—versus 2 to 3 percent charge-offs for typical bank consumer loans.

Who are these borrowers? 

Most earn less than $40,000 a year;  Younger millennials (25–34) account for about 30 percent of borrowers, with Gen Z adults (18–24) growing fastest;African Americans and Latinos are disproportionately represented;Many lack a college degree;  Single renters dominate the market;  Southern states and urban areas see the highest usage.  

The social consequences are profound. Payday loans often trap borrowers in cycles of reborrowing—over 80 percent of new loans are taken within 14 days of a previous loan. Repeat debit attempts by online payday lenders have been shown to trigger overdraft fees and even account closures for many borrowers. Transparency is another issue: borrowers rarely understand why they were approved or denied, and regulators have warned lenders that “black-box” models must still generate specific, understandable adverse-action reasons under federal law. 

And now, artificial intelligence is promising to amplify these problems. 

Uncommon Knowledge 

Artificial intelligence is revolutionizing small-dollar lending. What was once a manual, paperwork-heavy process is increasingly automated and decisioned in seconds. AI-driven platforms in this space—from loan marketplaces like RadCred (which uses AI-based matching and soft-pull prequalification) to cash-advance apps such as EarnIn, Dave, and Brigit—promise approvals and transfers in minutes by analyzing income stability, bank-account transaction patterns and other alternative signals beyond FICO. The result? Faster access to cash for borrowers and lower operational costs for lenders.  

Speed is the headline feature. RadCred markets “apply in 60 seconds” and instant AI matching to lenders. EarnIn’s Lightning Speed transfers arrive within around 30 minutes (for a fee). Brigit advertises Express Delivery “within 20 minutes.” Dave offers Express disbursement to debit for a percentage fee or free ACH by the next day. By contrast, many storefront and online payday lenders still fund the next business day via ACH (or same-day to debit in some cases), meaning access can vary from minutes to a day or more. For consumers facing urgent expenses—holiday gifts, travel, or medical bills—instant cash offers instant relief.  

But AI’s role isn’t just speed. By leveraging alternative data, AI can approve borrowers who lack conventional credit histories, theoretically expanding financial inclusion. The companies argue this reduces bias and democratizes access to credit. Consumer advocates counter that proxy variables (like location) can encode bias even when models exclude protected traits. 

The holiday surge magnifies these risks. Lenders actively market “Christmas loans” and other seasonal credit as applications rise through the fourth quarter. 

For an unknown number of payday borrowers, that strain probably increased during the government shutdown that ended in November after a record 43 days, with federal employees, welfare recipients, and others relying on government funding faced an unexpected cash crunch. On SoLo Funds—a peer-to-peer microlending platform—loan requests jumped 775 percent amid the shutdown/SNAP pause. It’s unclear what role, if any, AI-powered payday lenders played in shutdown-related borrowing. But they are structurally primed to profit from future crises with approvals at scale, targeted marketing and surge pricing—think Uber fares in a snowstorm. By Black Friday 2026, we’ll know what the AI arms race has done to America’s most vulnerable holiday shoppers. 

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