Buy Now Pay Later Fraud: A Growing Problem for Loan Providers

January 2024

Fintech & Payments

Matthew Purnell

Senior Research Analyst

BNPL fraud, simply, refers to any fraudulent activity conducted using Buy Now Pay Later. This fraud often takes two form: either fraudsters attacking payment systems or fraudsters exploiting the onboarding process of BNPL providers and merchants. BNPL providers are typically the party that is liable for losses through BNPL fraud, since they are the most likely to be the authoriser of the BNPL transaction.

Fraudsters have been quick to target BNPL due to not only because of its massive popularity, but also of the footholds that this microloan system provides for fraudsters. BNPL purchases often feature less roadblocks to the payment experience to avoid cart abandonment, specifically security checkpoints. This can make signing up for accounts, compromising logins, and taking over accounts easier for fraudsters.

The main safety pitfalls for BNPL transactions come in the form of real-time credit decisions, delays in repayment, or the absence of formal credit checks. For real-time credit decisions, BNPL providers have to approve purchase decisions when consumers complete transactions. This is a lightweight process that, whilst making the transaction frictionless, allows fraudsters to make large purchases with minimal resistance.

Delays in repayment also often allow bad actors to take advantage of extensive repayment windows to hack into accounts to make unauthorised transactions, paying the base value, and skipping the remaining payments. Finally, internal credit checks conducted using algorithms to determine creditworthiness are subject to errors, and these errors or improperly conducted checks can allow fraudsters to accomplish synthetic identity thefts.

In addition to how a lack of security checkpoints can be exploited by bad actors, there are several different types of BNPL fraud risk:

• Account takeover fraud – from stealing usernames to passwords through phishing, bad actors can gain access to a consumer’s account and place orders under the guise of a legitimate customer until the account holder notices.
• Synthetic identity fraud – synthetic identities are created by fraudsters by combining data that is freely available on people’s online profiles with false personal details, including fake names, dates of birth, and more. These synthetic identities pose as a legitimate customer to place orders using BNPL, with no intention of making repayments, and no way to find out who the bad actors are. As a result, BNPL companies typically write these defaulted payments off as bad debt.
• New account fraud – making a new account with a BNPL provider is easy, and fraudsters can use stolen information through hacking or other data breaches to make purchases. This is a prominent challenge for BNPL companies because their KYC (Know Your Customer) and anti-money laundering checks are not robust enough to detect these bad actors.
• Non-repayment fraud – by combining the aforementioned methods, fraudsters place orders with no intention of paying back the loan outside of the initial payment.
• Refund abuse and friendly fraud – friendly fraud involves consumers requesting a refund for a product that they do not intend to return, likely making a false claim that they do not recognise the transaction to initiate a chargeback and then keep the product and money.

The most common ways that BNPL fraud can be prevented include identity verification and authentication methods. Identity verification is the first barrier, due to the mandatory KYC checks conducted on customers aimed at preventing fraud. These should normally require customers to provide a form of ID and documentation like proof of address. Authentication methods assist the verification of a cardholder’s identity through the likes of multi-factor authentication in the form of biometrics, SMS, passwords, and more to confirm the identity of a consumer before authorising a payment.

Transaction monitoring is another form of fraud prevention, where merchants or BNPL providers examine activity to detect potential red flags, such as logins from different IP addresses or devices, multiple payment attempts using the same card, or attempts at using details that have been reported as stolen. This data forms the basis of machine learning-based fraud detection and prevention.

Source: Buy Now Pay Later Market 2024-2028

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