Hello! For educational purposes, let's take a closer look at how the rise of
Buy Now, Pay Later (BNPL) services —such as Klarna, Affirm, Afterpay, and similar services—impacts
carding strategies. Carding is a form of financial fraud in which criminals use stolen credit card information (numbers, CVV, expiration dates) to make unauthorized purchases. This isn't a new practice, but BNPL adds new layers of complexity, making it more attractive to fraudsters.
BNPL works on an installment plan: the consumer pays a portion of the amount upfront (often 25%), and the remainder in several interest-free payments (if made on time). This is convenient for shopping, but it creates an opportunity for abuse, as credit checks are often soft, and full payment is deferred. By 2025, the global BNPL market is expected to exceed $576 billion, with 91.5 million users in the US, increasing pressure on security systems. We will examine this impact step by step, relying on trends, statistics and mechanisms, without providing practical instructions.
Why the rise of BNPL is driving the evolution of carding strategies
The growth of BNPL directly correlates with the growth of online shopping: according to 2025 estimates, BNPL is used in 9% of all e-commerce transactions in the US. This creates a favorable environment for carders for several reasons:
- Payment deferrals as a "buffer" for fraud: With traditional credit card carding, fraud is often detected quickly—the bank blocks the card after a suspicious transaction. With BNPL, the initial payment is small, and the remaining payment is charged later (after 2-6 weeks). Fraudsters can make a purchase, receive the goods, and "disappear" before the BNPL provider or bank notices the problem. This prolongs the lifespan of the stolen data: carders monetize the goods (for example, by reselling them on the secondary market) until the victim discovers the breach.
- Soft checks and synthetic identities: BNPL services often rely on "soft" credit checks, which don't affect credit scores and don't require in-depth analysis. This allows carders to create synthetic identities —a combination of real (stolen) and fictitious data. For example, card details from the dark web are combined with fake addresses and email addresses. Synthetic fraud in BNPL is expected to increase by 26% by 2025, as a single set of data can be used for multiple accounts.
- E-commerce integration and scalability: BNPL is built into platforms like Amazon or Shopify, facilitating automated attacks. Carders use bots to test stolen cards (card checking) in BNPL, where low initial fees reduce the risk of being blocked. This leads to "fraud-as-a-service"—services on the darknet where newcomers purchase ready-made BNPL exploitation schemes.
Table: Comparison of traditional carding and carding with BNPL
| Aspect | Traditional carding (with credit cards) | Carding with BNPL |
|---|
| Speed of detection | Fast (minutes/hours, through bank fraud monitoring) | Deferred (weeks/months, until payments are collected) |
| Checks | Hard credit checks (3D Secure) | Soft (soft check, minimal data) |
| Scale | Limited by card limits | Scaling (multiple accounts with synthetic IDs) |
| Risks for a fraudster | High (fast lock) | Low (time to monetize) |
| Product examples | Electronics, gift cards | Large purchases (furniture, gadgets), resale |
Specific strategies for exploiting BNPL in carding
Carders are adapting strategies for BNPL, focusing on vulnerabilities. Here are the key methods (described hypothetically to understand the mechanism):
- Account Takeover (ATO): Fraudsters take over existing user accounts through phishing (fake emails/SMS) or credential stuffing (automated testing of stolen passwords). They then use pre-approved BNPL limits for purchases. In 2025, ATO is the leading method in BNPL fraud, as it switches the payment method to a stolen card after the first legitimate payment, disguising the attack.
- First-Party Fraud: This is a form of self-deception where the "buyer" (carder) intentionally fails to pay after receiving the goods. BNPL incurs losses because it cannot collect the debt from the fake account. In the US, 23% of application fraud cases from 2021 to 2025 are related to fake BNPL accounts, and in 2025, 41% of BNPL users reported late payments.
- Phishing and data collection for BNPL: Carders use phishing to obtain the data needed to create accounts. For example, fake websites simulate BNPL registration, collecting data for real attacks.
- Chargeback Exploitation: After a purchase, carders initiate a chargeback (transaction dispute) on the stolen card, returning the money but keeping the goods. BNPL providers will lose up to $206 billion from online fraud by 2025, with a 22% increase in BNPL fraud.
- Bot-Driven Attacks and Synthetic Fraud: Bots automate the creation of accounts with synthetic IDs. Fragments of real identities (SSNs, addresses) are combined to bypass verification.
Statistics and trends for 2025
- Fraud growth: BNPL fraud has grown by 106% by 2028 (based on a 2025 forecast). In 2025, the BNPL fraud rate will be 3–4%, with losses of $4.61 per dollar of fraud in the US.
- Demographics: 36% of BNPL users use it for cash flow, 28% for large purchases, but the regret rate is 40%.
- Global Trends: Cross-border fraud is on the rise due to the integration of BNPL into global payments.
Table: Key BNPL Fraud Statistics in 2025
| Metrics | Meaning | Source |
|---|
| The global BNPL market | >$576 billion | GlobalData |
| Users in the US | 91.5 million | Worldline |
| Rising BNPL Fraud | +22% | CoinLaw |
| Late payments among users | 41% | CoinLaw |
| Total losses from online fraud | $206 billion | Infosys |
Ecosystem Impact and Countermeasures
- For merchants: They lose goods and incur chargeback costs, but BNPL increases sales conversion by 20–30%.
- Consumers: Risk of identity theft; 21% with a credit history used BNPL in 2022–2025, with regret rising.
- For providers: Low default rates (below credit cards), but growing fraud requires investment in AI.
Measures: Biometrics, AI monitoring, 2FA, partnerships with banks. Regulators (CFPB) are introducing strict rules for protection.
Conclusion
The rise of BNPL has revolutionized payments, but it has also strengthened carding, making it more discreet and scalable. Understanding these mechanisms helps us understand the risks of the digital economy. For consumers: monitor your accounts and use strong passwords. This is an educational analysis; in reality, the focus is on the ethical use of technology.
