The Growing Threat of BNPL Fraud and How to Counter It

By allowing shoppers to split their purchases into interest-free installments, BNPL options provide an attractive alternative to traditional credit. Companies like Klarna, Afterpay, and Affirm have become household names, offering convenient payment solutions that appeal to a wide range of consumers, from millennials to older adults.

The appeal of BNPL lies in its simplicity and flexibility. Consumers can make purchases immediately and pay over time without the burden of interest rates typically associated with credit cards. This ease of use has led to a rapid adoption of BNPL services, with millions of users worldwide and significant growth in transaction volumes.

However, the rise in BNPL usage has also brought about a surge in fraudulent activities. Fraudsters are continually finding new ways to exploit these services, posing substantial risks to both consumers and businesses. This article delves into the various types of BNPL fraud, their implications, and the strategies that can be employed to mitigate these risks.

What is buy now pay later fraud?

Buy Now Pay Later (BNPL) fraud encompasses a range of deceptive activities where fraudsters exploit the BNPL payment model to obtain goods and services without intending to pay for them. As BNPL services have become more popular, the methods used by fraudsters have become increasingly sophisticated, making it a critical issue for both consumers and businesses.

One common type of BNPL fraud involves the creation of fake accounts using stolen or synthetic identities. Fraudsters may use these accounts to make purchases and then disappear, leaving merchants and BNPL providers to absorb the losses. Another tactic involves account takeovers, where fraudsters gain unauthorized access to legitimate BNPL accounts and use them to make fraudulent purchases.

Understanding the mechanisms behind BNPL fraud is essential for developing effective prevention strategies. Fraudsters often exploit the rapid approval processes and minimal credit checks associated with BNPL services, making it easier for them to slip through the cracks. As BNPL continues to grow, so does the importance of robust fraud detection and prevention measures.

Most common types of buy now pay later fraud

When does BNPL fraud even occur? Fraudsters typically exploit Buy Now Pay Later systems in a variety of ways. For instance, they can easily create a new account with stolen credentials to exploit a BNPL offer. Another common case is when a fraudster takes over an existing BNPL account, which is normally used by a legitimate user. Money muling is also connected to this type of crime. Scammers can utilize a BNPL account to launder funds by engaging in money muling; their objective is to obscure the source of the funds (if you wish, you can discover exactly what are the money mule red flags and how layering in money laundering works). 

New account abuse (new account fraud)

New account abuse, also known as new account fraud, involves fraudsters creating fake accounts to exploit BNPL services. This type of fraud typically uses stolen personal information or entirely fabricated identities to open accounts and make purchases without intending to repay.

This particular type of fraud can create financial losses for merchants and BNPL providers, increase operational costs due to fraud management and recovery efforts, and erosion of consumer trust in BNPL services.

Synthetic identity fraud

Synthetic identity fraud occurs when fraudsters create new identities by combining real and fake information. For example, they might use a legitimate social security number paired with a fabricated name and birthdate. This synthetic identity is then used to apply for BNPL services and make purchases.

Similarly to new account fraud, the consequences of synthetic identity fraud can trigger financial losses for businesses as these synthetic identities often go undetected for extended periods. Another problem is the difficulty in tracking and identifying fraudsters due to the mix of real and fake data.  

Account takeover (ATO)

Account takeover fraud involves fraudsters gaining unauthorized access to existing BNPL accounts. They often achieve this through phishing attacks, credential stuffing, or exploiting weak security measures. Once inside, they can make purchases, change account details, and drain any linked payment methods.

Of course, it is well-known that account takeover leads to direct financial losses from fraudulent transactions, in addition to long-term reputational damage loss of consumer confidence, and increased costs associated with fraud investigation and account recovery.

Regulatory implications and compliance challenges

As BNPL services become more popular, they are increasingly subject to regulatory scrutiny. Fraud incidents can trigger regulatory investigations and lead to stricter compliance requirements, which can be both costly and time-consuming for businesses.

Increased regulation: regulatory bodies may impose additional requirements on BNPL providers to protect consumers from fraud.

Compliance costs: meeting these regulatory requirements can be expensive, requiring significant investment in compliance programs and technology.

Legal consequences: failure to comply with regulations can result in legal action, fines, and further reputational damage.

How can digital signals prevent BNPL fraud

Digital signals, such as email, phone, IP, browser, and device data, play a critical role in detecting and preventing BNPL fraud. By leveraging these signals, businesses can enhance their fraud detection capabilities and make more informed decisions.

Control customer acquisition costs (CAC): effective fraud prevention strategies can help keep customer acquisition costs under control. By eliminating fraudulent leads from marketing efforts, businesses can ensure that their resources are directed towards genuine customers, ultimately improving the efficiency of their marketing campaigns.

Verify identities: pre-screening applicants with precision can help identify synthetic identities and prevent fraudsters from applying for loans or making purchases through BNPL services. This involves using advanced identity verification techniques that analyze digital footprints and other trust signals.

Improve decision-making: enriching decision engines with digital signals allows businesses to better understand customer risk profiles. By incorporating data such as email, phone, IP, and browser behavior, companies can make more confident loan approvals and reduce the risk of fraud.

Leveraging risk intelligence against fraud

As BNPL services continue to grow, the need for advanced fraud detection methods becomes increasingly crucial. Digital signals—such as email, phone, IP, browser, and device data—play a vital role in identifying and preventing BNPL fraud. These signals, when analyzed using sophisticated machine learning algorithms, help create detailed trust and risk profiles, enabling providers to make more informed decisions.

Email data

Email data is unlocked through Email Address Analytics:

Usage: verifying the legitimacy of email addresses used in BNPL applications.
Detection: identifying suspicious patterns, such as newly created email addresses or those frequently associated with fraudulent activities.
Outcome: improved accuracy in detecting fake or compromised accounts.

Phone data

Phone data is unlocked through Phone Number Intelligence:

Usage: validating phone numbers provided by users during the application process.
Detection: cross-referencing phone number histories to identify inconsistencies or anomalies indicative of fraud.
Outcome: enhanced ability to spot and prevent synthetic identities and account takeovers.

IP data

IP data is unlocked through IP Address Analysis: 

Usage: analyzing IP addresses to determine the geographic location and assess risk.
Detection: flagging IP addresses linked to high-risk regions or known fraudulent activities.
Outcome: reduction in fraud originating from suspicious locations or proxy servers.

Browser data

Browser data is unlocked through Browser Fingerprinting:

Usage: monitoring browser behavior and device characteristics.
Detection: identifying unusual browser configurations or patterns that deviate from typical user behavior.
Outcome: detection of automated attacks or the use of emulators and bots.

Device data

Device data is unlocked through Device Detection:

Usage: assessing device information such as hardware, software, and operating system.
Detection: detecting multiple accounts created from a single device or rapid switching between different devices.
Outcome: preventing fraudsters from using the same device to perpetrate multiple fraudulent activities.

ML Algorithms and Trust/Risk Scores

Machine learning algorithms are instrumental in processing and interpreting digital signals. These algorithms analyze vast amounts of data to identify patterns and anomalies that may indicate fraudulent behavior. By continuously learning from new data, these systems can adapt to emerging fraud tactics, providing robust and up-to-date protection.

Trust scores: calculated based on the analysis of digital signals, trust scores help determine the likelihood that a BNPL applicant is legitimate.

Risk scores: similarly, risk scores indicate the probability of fraudulent activity, guiding providers in making informed decisions.

Closing thoughts

As Buy Now Pay Later (BNPL) services continue to expand, it is imperative to clarify the types of BNPL fraud. Now that we’ve gone over this, the next step is to develop effective fraud prevention and fraud detection strategies for digital lending organizations. With the help of digital signals and advanced ML algorithms, BNPL providers can enhance their fraud detection capabilities, protect their customers, and maintain regulatory compliance.

The adoption of comprehensive identity verification, real-time monitoring, and adaptive learning systems, combined with strategic risk management, can significantly mitigate the risks associated with BNPL fraud.

To further expand these defense mechanisms for BNPL fraud prevention, reach out to our team of fraud experts.

Questions and Answers

What are the most common indicators of synthetic identity fraud in BNPL applications?
Common indicators of synthetic identity fraud include inconsistencies in personal information, such as mismatched names and birthdates, and the use of newly created email addresses or phone numbers. These synthetic identities often have thin credit files, which can be a red flag for fraud detection systems.

How can businesses effectively use machine learning to prevent BNPL fraud?
Businesses can leverage machine learning algorithms to analyze vast amounts of data, identifying patterns and anomalies indicative of fraud. By continuously learning from new data, these systems can adapt to emerging fraud tactics, providing robust and up-to-date protection against fraudulent activities.

What regulatory challenges do BNPL providers face in combating fraud?
BNPL providers must navigate a complex regulatory landscape, which may include stricter compliance requirements and increased scrutiny from regulatory bodies. Meeting these requirements often involves significant investment in compliance programs and technology to ensure that consumer protection standards are upheld.

How can digital signals improve the accuracy of fraud detection in BNPL services?
Digital signals, such as email, phone, IP, browser, and device data, provide valuable insights into the legitimacy of BNPL applications. By analyzing these signals, businesses can detect suspicious patterns and anomalies, leading to improved accuracy in identifying and preventing fraudulent activities. Integrating these signals into decision-making processes helps create comprehensive risk profiles for more informed decisions.