Pay Later Credit Option May Have to Win Over the Algorithm First

Artificial intelligence (AI) is beginning to influence a part of commerce that has traditionally belonged to consumers: choosing how to pay.

For years, Pay Later providers competed for attention at checkout through marketing, merchant placement and promotional financing. New PYMNTS Intelligence research with Splitit suggests that if AI shopping assistants become responsible for recommending financing options, providers will increasingly compete to satisfy algorithms that weigh cost, credit impact and consumer preferences before presenting a recommendation.

According to the research, 61% of U.S. consumers would consider allowing an AI shopping assistant to recommend a Pay Later option for at least one purchase category, while 39% would reject the idea entirely. Younger consumers are substantially more receptive, with 80% of Gen Z and 78% of millennials expressing openness to AI-driven Pay Later recommendations.

The appeal of Pay Later helps explain why consumers are prepared to involve AI in the process.

Financing choices have become increasingly complicated as traditional credit cards, card-linked installment plans and BNPL products often coexist at checkout. Comparing interest costs, repayment schedules, rewards, credit implications and promotional offers requires time that many consumers would rather spend completing a purchase. AI promises to reduce that burden by evaluating available options almost instantly.

Yet consumers are not asking AI to make independent financial decisions. They are looking for assistance that simplifies comparison while leaving the final decision in their hands.

Algorithms Will Need Different Priorities

If AI increasingly recommends financing, the next question becomes straightforward: Which payment options will those algorithms select first?

The research hints that the answer may depend less on marketing budgets than on measurable consumer outcomes.

Consumers consistently place affordability ahead of rewards. Among respondents willing to consider AI-driven Pay Later, 24% want AI to select the most affordable option, and an identical share want the technology to require their approval before any financing plan is finalized. Only 18% prioritize maximizing credit card rewards or cash back.

The indication is that consumers expect AI to optimize for total borrowing cost rather than promotional incentives. The pattern continues when consumers identify the features they consider most important. Fifty-nine percent say protecting their credit score is highly important. Fifty-six percent want AI to identify the lowest total borrowing cost, while 54% prioritize the most affordable monthly payment. More than half also want AI to avoid opening new credit lines and instead use their existing credit cards whenever possible.

BNPL providers, card issuers and installment programs have traditionally competed through visibility at checkout and consumer marketing. If AI becomes an intermediary that evaluates financing options before consumers ever see them, providers may need products that consistently perform well against objective criteria such as cost, credit preservation and repayment flexibility.

The data also suggests that AI recommendations could vary by purchase category. Electronics lead consumer willingness to accept AI-selected Pay Later options, with 17% expressing interest. Furniture, apparel, travel and everyday essentials each attract roughly 13%, while medical expenses, home repairs and auto-related purchases also generate meaningful interest.

Trust remains the limiting factor.

Consumers repeatedly signal that they want oversight before automation. Twenty-eight percent say approval before purchase completion would increase trust in AI recommendations, followed by confirmation of total cost before purchase at 25%. Access to human support, confirmation of personal data security and clear explanations of recommendation logic also rank highly. Meanwhile, 34% say no factor would make them trust AI-driven Pay Later recommendations, underscoring that skepticism remains substantial across parts of the market.

If recommendation engines consistently favor lower costs, credit preservation and familiar financial relationships, the financing products that best meet those standards could gain a meaningful advantage before a shopper ever reaches the checkout screen.

At PYMNTS Intelligence, we work with businesses to uncover insights that fuel intelligent, data-driven discussions on changing customer expectations, a more connected economy and the strategic shifts necessary to achieve outcomes. With rigorous research methodologies and unwavering commitment to objective quality, we offer trusted data to grow your business. As our partner, you’ll have access to our diverse team of PhDs, researchers, data analysts, number crunchers, subject matter veterans and editorial experts.