A new report by The Kaplan Group examines how hidden consumer liabilities—especially Buy Now, Pay Later (BNPL) loans that do not appear on traditional credit reports—are building risk of default and creating “ghost debt” that will spill over into commercial credit. It combines Google Trends search‑trend signals with recent Federal Reserve, Consumer Financial Protection Bureau (CFPB), and Fed regional bank data to answer a central question: when consumers increasingly rely on unreported BNPL credit, how does that stress propagate into late business payments, higher collections volume, and small‑business failure risk?
Key Takeaways
- The search interest for “buy now pay later” has stayed at moderate‑to‑high interest since 2020, with the last 12 weeks of 2025 running well above the five‑year baseline (recent average index 73.4 vs. overall 53.6).
- A majority of BNPL users report using it because it was the only way they could afford their purchase, with especially high necessity‑use rates among households with income under $50,000.
- Roughly one‑third of BNPL borrowers hold loans with multiple BNPL providers and a majority originated multiple loans in a short period.
How is BNPL demand changing?
From late 2020 to late 2025, search interest in the full phrase “buy now pay later” has remained consistently moderate to high, reflecting widespread consumer awareness and active use of BNPL products. Using Google Trends for the United States, the phrase reached its five‑year peak (index 100) around December 11, 2022, but the more recent pattern shows elevated levels rather than a return to pre‑peak norms.
- Search‑trend overview (2020–2025).
- Google Trends for “buy now pay later” in the U.S. from late 2020 through late 2025 shows persistent, moderate‑to‑high interest rather than a short‑lived spike.
- The phrase reached its maximum index value (100) around December 11, 2022, during a holiday spending surge.
- The BNPL acronym starts near zero in late 2020 (weekly values around 0–4) but climbs steadily into double digits by mid‑2021 and then into the 20–40 range through much of 2022–2023, indicating growing industry and media attention.
- Recent baseline shift.
- Over the last 12 weeks of 2025, the average index level for “buy now pay later” is roughly in the low‑70s, compared with a five‑year average in the low‑50s, indicating a step‑up in everyday dependence on BNPL.
- This pattern suggests that households are not backing away from BNPL post‑pandemic; instead, they are normalizing it as an ongoing liquidity tool.
- By mid 2025, weekly “BNPL” interest is routinely in the 20–40 range and then accelerates into the 40–70 band, culminating in a peak of 100 in the week of November 30, 2025, which marks its highest recorded level and reflects intense scrutiny and concern around BNPL.
Geographically, the top states for “buy now pay later” search interest skew toward the South, including Mississippi, West Virginia, Arkansas, Alabama, and Louisiana, aligning with regions that also exhibit higher measures of household financial stress and commercial credit risk.
What does the data say about “Ghost Debt” and financial distress?
BNPL‑driven “Ghost Debt” shows up as a large, mostly invisible layer of liability that is both common and heavily concentrated among already‑stressed households. Each datapoint below underscores how often BNPL is used out of necessity, how frequently it is stacked on top of other credit, and how severe financial strain looks by the time problems surface.
- BNPL loans usually remain off traditional credit reports unless they go to collections, meaning large BNPL balances can be invisible in risk models until they are already 90+ days delinquent or in charge‑off status.
- Federal Reserve research finds that about 55–58% of BNPL users say it was the “only way I could afford” a purchase, with necessity‑driven use especially common among households with low income and limited liquid savings.
- In 2022, roughly 21% of consumers with a credit record used BNPL at least once, and about one‑fifth of those users were “heavy” borrowers taking out multiple BNPL loans per month, typically on top of existing credit‑card and personal‑loan balances.
- Regional Federal Reserve analysis shows that nearly all BNPL users who paid late exhibit at least one indicator of financial constraint, and a sizable share of late‑pay users fall into a “severely constrained” category with several overlapping stress markers (e.g., high utilization, low savings, prior delinquencies).
How does “Ghost Debt” translate into commercial bad debt?
The same households accumulating hidden BNPL obligations are also employees, customers, and owners of small businesses, so their stress quickly “trickles up” into missed invoices and B2B delinquencies. The data points below highlight how personal leverage, tighter bank credit, and a short transmission window turn consumer BNPL strain into commercial bad debt.
- Surveys and credit‑file studies show that small‑business owners with significant personal debts (such as student loans) are about 30–35% more likely to be delinquent on commercial obligations than otherwise similar owners without those burdens.
- Federal Reserve Small Business Credit Survey data indicate that the share of small firms carrying more than $100,000 in debt remains above pre‑pandemic levels, and existing debt is now a top reason banks deny new credit requests.
- Case studies and lender reports suggest that when BNPL and other household obligations are prioritized, local businesses can see revenue declines severe enough to trigger missed supplier payments within 30–60 days, creating a short lag from consumer stress to B2B placements.
- Together, these patterns imply that widespread, under‑reported BNPL use among consumers raises the probability that small firms will miss invoices, contributing to a higher‑than‑baseline rate of commercial accounts entering collections over the following one to two billing cycles.
“Ghost Debt” as a forward‑looking risk signal
Taken together, the evidence paints “Ghost Debt” as a large, under‑reported layer of leverage that is both widespread and highly concentrated among already stressed households. BNPL use is common—roughly one in five consumers with a credit record have used it—and a significant minority are heavy users stacking multiple loans on top of existing credit‑card and personal‑loan balances.
For commercial creditors, the link is direct: highly leveraged, liquidity-constrained consumers are also customers, employees, and owners of small firms, and their stress flows through into weaker sales, stretched receivables, and higher B2B delinquencies within one to two billing cycles. Monitoring BNPL intensity, necessity‑driven use, and late‑payment patterns by region and segment therefore offers a practical early‑warning system for 2026 commercial defaults, rather than a niche consumer‑finance curiosity.
Methodology
This analysis combines a digital‑demand signal from Google Trends with consumer‑finance and small‑business data from the Federal Reserve System, the CFPB, and affiliated research to describe how “Ghost Debt” has evolved and how it may influence commercial credit outcomes.
Google Trends was used to track U.S. search interest for the spelled‑out query “buy now pay later” from December 2020 through December 2025, using the standard 0–100 index where 100 represents peak relative interest over the chosen period. Summary statistics include the five‑year peak value and timing, the overall five‑year average index level, and the average index over the most recent 12 weeks to capture any baseline shift in BNPL‑related interest. State‑level interest was examined to identify geographic clusters where “buy now pay later” search intensity is highest and to compare those regions qualitatively with known pockets of household and commercial credit stress.
On the outcomes side, the report draws primarily on Federal Reserve Board research on BNPL usage motives and financial well‑being, the CFPB’s 2025 report on consumer use of BNPL and other unsecured debt, and the Federal Reserve Bank of Kansas City’s work on financial constraints among BNPL users. These sources provide estimates of the share of adults using BNPL, the fraction who are “heavy” or repeated users, the proportion citing necessity (“only way I could afford it”), and the prevalence of overlapping indicators of financial strain such as low savings, high utilization, and prior delinquencies. Small‑business risk is described using the Federal Reserve’s Small Business Credit Survey—particularly statistics on debt loads above $100,000 and reasons for credit denial—as well as Experian’s analysis of how owners’ personal debts correlate with commercial delinquencies.
All figures are reported as‑published point estimates or simple percentages, with no additional weighting or causal modeling applied. The analysis is descriptive and exploratory: it does not attempt to estimate precise causal effects of BNPL on commercial default, but instead maps co‑occurring patterns in demand, usage, financial constraints, and small‑business credit outcomes to identify plausible transmission channels and leading indicators for 2026 B2B risk.
