Alors que la France est en pleine période des soldes d'hiver, les professionnels peuvent se demander : cette tradition est certes appelée à perdurer, mais qu'en est-il des habitudes d'achat des consommateurs pendant cette période ? Les clients ont depuis longtemps délaissé les soldes traditionnelles au profit de la commodité des soldes en ligne. On peut donc affirmer sans se tromper que l'avenir s'oriente vers une expérience client toujours plus facile. C'est là qu'intervient Agentic AI... Mais si les soldes ont augmenté la fréquentation des magasins physiques, elles ont également attiré leur lot de voleurs à l'étalage...
Once a niche payment option, Buy Now, Pay Later (BNPL) has boomed across ecommerce and retail. BNPL has quickly become as much a part of the checkout experience as quick pay options and saved cards.
In fact, 380 million people use BNPL services globally. This is predicted to increase to 670 million by 2028. This rapid growth brings fresh challenges: more users, higher transaction volumes, and lighter onboarding controls create fertile ground for fraudsters to test and exploit gaps in risk controls.
For payments professionals, that means balancing the demand for fast, frictionless customer experiences with the need to protect revenue, manage chargebacks, and reduce operational strain.
Let’s break down what BNPL fraud looks like in practice, explore why it’s a growing concern for ecommerce teams, and share practical strategies your business can adopt to prevent fraud without undermining conversion.
What Is BNPL Fraud and Why It’s a Growing Concern
BNPL fraud refers to activity that exploits Buy Now, Pay Later payment systems to deceive merchants or providers and obtain goods, services, or credit without intending to pay back. Fraudsters take advantage of BNPL’s lightweight onboarding and delayed settlement structure, making it easier to abuse the system compared with traditional payment methods.⁴⁾ Common examples include:
- Identity misuse and synthetic identities — criminals use stolen personal information or fabricated identities to open BNPL accounts or complete purchases.
- Friendly or first-party fraud — an initially legitimate user who abuses the system by taking out BNPL credit with no intention of repaying or disputes legitimate transactions to force a refund.
- Account takeover (ATO) — fraudsters compromise a legitimate user’s account to make purchases without the owner’s knowledge.
Similarly to traditional card fraud, BNPL fraud often begins in the onboarding process and unfolds over weeks or months, because the payment is deferred and the provider only learns of non-repayment later. This difference makes detection and response more complicated than instant card-based transactions.
Why BNPL Fraud Is Growing
Several structural features of BNPL heighten the fraud risk:
- Frictionless onboarding lowers barriers: BNPL services prioritise speed and ease at checkout, often with limited identity checks, which fraudsters can exploit to slip through risk controls.
- Rapid volume growth expands attack surface: As BNPL adoption continues to surge worldwide, fraudsters increasingly target these platforms with both traditional tactics and new schemes like bot-based application attacks.-making according to Visa.
The combination of convenience for legitimate customers and exploitable vulnerabilities for bad actors is why BNPL fraud continues to rise, making robust prevention strategies essential for payments and fraud teams.
Top BNPL Fraud Problems and Why Traditional Controls Struggle
BNPL fraud continues to grow as Buy Now, Pay Later becomes a more common payment method in ecommerce. The same features that make BNPL attractive to shoppers — fast onboarding and deferred payments — also introduce gaps that fraudsters can exploit. Traditional fraud controls, designed for older payment models, often aren’t built to address these unique dynamics.
Identity Abuse and Synthetic Identities
One of the most common BNPL fraud problems is identity exploitation. Fraudsters use stolen personal information or create synthetic identities by combining real data with fake details.
Because many BNPL systems aim for minimal friction at signup, these fraudulent profiles can slip through basic checks, be used to make purchases, and then be abandoned before repayment is due.
Strategic Misuse by Customers
Not all BNPL fraud originates outside the customer base. Friendly fraud occurs when a shopper disputes a legitimate BNPL transaction, claiming it was unauthorized in order to trigger a refund.
Because the customer is legitimate and uses legitimate payment methods, resolving these cases can be trickier than straightforward fraud cases, and can increase operational workload and losses for merchants.
Account Takeover and Rapid-Fire Attacks
Another frequent risk is account takeover (ATO), where fraudsters gain access to legitimate user accounts through stolen credentials, phishing, or credential stuffing. Relatedly, automated attacks can submit multiple BNPL applications in quick succession from the same device or network in an attempt to overwhelm simple rule-based systems.
Traditional fraud tools, optimised for instant credit card authorisation, often miss these patterns because the risk doesn’t fully manifest at the moment of checkout.
Why Traditional Controls Fail to Tackle BNPL Fraud
Legacy fraud systems primarily rely on static rules and historical payment signals that work reasonably well for immediate card transactions. They struggle when signals are subtle, delayed, or behavioural in nature, common characteristics of BNPL fraud.
They also often force a trade-off between false positives (blocking legitimate customers) and false negatives (missing real fraud), which can hurt conversion rates and customer experience.
This combination of new fraud patterns and outdated controls is why many teams are moving toward modern, adaptive BNPL fraud prevention tools that can assess risk in real time, incorporate identity intelligence, and monitor behaviour throughout the payment lifecycle.
How to Improve BNPL Fraud Prevention
Effective BNPL fraud prevention requires more than adapting traditional card-based controls. Because Buy Now, Pay Later models combine fast onboarding with deferred repayment, risk teams need layered, real-time approaches that assess identity, behaviour, and transaction context together.
Strengthen Identity Verification at Onboarding
Lightweight signup processes are attractive to customers and fraudsters alike. That’s why identity verification should go beyond basic email and address checks. This includes evaluating device intelligence, email and phone reputation, behavioural signals and consistency across data points.
The goal is not to introduce heavy friction for every customer, but to build a clearer picture of whether a user is genuinely who they claim to be.
Use Real-Time Risk Scoring at Key Decision Points
BNPL fraud often begins at account creation or first purchase. Real-time fraud detection at onboarding and checkout allows teams to assess risk instantly, rather than waiting for repayment failures or disputes to reveal problems later.
Adaptive risk scoring, based on identity signals, transaction context, and behavioural patterns, helps teams act before losses occur.
Monitor Velocity and Anomalous Behaviour
Fraudsters frequently test systems by submitting multiple applications, using the same device across different identities, or making rapid, high-value purchases.
Effective BNPL fraud prevention includes monitoring for velocity spikes, unusual account patterns, and inconsistencies across sessions. These behavioural indicators often reveal organised activity that static rule sets miss.
Extend Controls Beyond the First Transaction
Because BNPL revenue is deferred, fraud risk doesn’t end at checkout. Ongoing monitoring — including changes in device, payment details, delivery address, or repayment behaviour — helps identify account takeover, friendly fraud, or emerging repayment risk before it escalates. Continuous fraud detection reduces the likelihood that issues only surface weeks later.
Balance Risk Reduction with Customer Experience
Perhaps the most important principle is balance. Overly aggressive controls can create high false positives, blocking legitimate buyers and damaging conversion rates.
Intelligent BNPL fraud prevention uses risk-based step-ups, applying stronger checks only when signals justify them, to protect revenue without undermining the seamless experience customers expect.
Why This Matters for Payments and Fraud Teams
Stronger BNPL fraud prevention delivers more than lower fraud rates;
It directly reduces losses, chargebacks and write-offs, protecting margins in a payment model where repayment risk is already deferred.
Secondly, better fraud detection lowers manual review volumes, reducing operational strain and allowing teams to focus on higher-value investigations rather than processing borderline cases.
It’s also worth remembering that by minimising unnecessary declines, you improve approval rates and customer experience, supporting conversion and repeat purchase behaviour.
Finally, more sophisticated risk controls strengthen your compliance and partner relationships, demonstrating responsible credit management in a regulatory environment that continues to evolve.
In short, effective BNPL fraud prevention is not just a defensive measure — it’s a strategic enabler of sustainable growth. By reducing losses while preserving customer experience, payments teams can support long-term revenue expansion without increasing exposure to fraud risk.
Start Building your BNPL Fraud Protection Strategy
The answer isn’t heavier, more disruptive checks. It’s balanced, intelligent BNPL fraud prevention that combines strong identity signals, real-time fraud detection and ongoing monitoring without undermining the customer experience.
If done well, these measures shouldn’t affect your growth, they should actually complement it. As BNPL adoption continues to expand, teams that modernise their approach to fraud risk will be best positioned to reduce losses, maintain approval rates and support sustainable revenue growth.
For deeper insight into related identity and payment fraud challenges, explore our other resources on digital identity verification and fraud prevention strategies.
If I (Sébastien Carletti) were to elaborate on the BNPL fraud subject, here would be my take:
BNPL Fraud Prevention: Key Risks and How Businesses Can Reduce Exposure
What is BNPL Fraud?
Buy Now, Pay Later (BNPL) solutions have rapidly transformed the ecommerce landscape, allowing customers to split payments into instalments while merchants benefit from higher conversion rates and larger basket sizes.
However, the rapid growth of BNPL has also attracted new forms of fraud and misuse. The deferred nature of the payment creates a structural gap between the purchase and the moment the financial risk materialises.
Traditionally, fraud in digital payments refers to situations where an attacker impersonates or takes control of a legitimate user in order to obtain goods or services without paying. In BNPL environments, this remains a major threat.
But BNPL also introduces a second category of risk: situations where the customer is genuine and correctly identified, yet repayment may never occur.
Understanding BNPL risk therefore requires distinguishing between professional fraud and strategic misuse by legitimate customers.
Why BNPL Fraud is Increasing
Several factors contribute to the growing exposure of BNPL providers.
Frictionless onboarding
BNPL services are designed to reduce friction at checkout. While this improves customer experience, it also lowers the barrier for attackers attempting to create accounts or apply for credit using stolen or synthetic identities.
Deferred payment models
Unlike traditional card payments, the financial risk in BNPL appears later in the transaction lifecycle. This delay gives both fraudsters and opportunistic users more time before repayment obligations become visible.
Rapid market adoption
As BNPL becomes a mainstream payment option, the attack surface naturally increases. More users, more merchants, and more transactions mean a broader environment where both organised fraud and behavioural misuse can occur.
Key BNPL Risk Categories
