Real-time solutions to combat fraud in BNPL

Digital Fraud January 2022 - Find out how BNPL providers can proactively secure transactions against fraud using AI and machine learning

The growing popularity of buy it now, pay later (BNPL) has made fraud prevention an increasingly important topic for providers and merchants who offer it. Twenty-five percent of American merchants have already I accept BNPL payments, and 46% plan to implement it as an option in the coming year, expecting increased sales, customer loyalty and larger baskets at checkout.

At the start of the 2021 holiday shopping season, 12% of consumers said foreseen to fund at least a portion of their purchases using BNPL, and many Millennials, Gen Xers, and Gen Z consumers planned to make BNPL their primary payment option for holiday purchases. A notable 45% of BNPL users – nearly half – said they destined to use BNPL for all or part of their holiday shopping, indicating how popular it has become in recent months.

This month’s Deep Dive examines how BNPL has attracted not only consumers but fraudsters as well, and illustrates the vulnerabilities these fraudsters are driven to exploit. It also examines the proactive steps BNPL suppliers and merchants can take to prevent fraud, stopping fraudulent activity as it occurs.

Fraud vulnerabilities present in BNPL transactions

As installment payment options have grown in popularity, some of BNPL’s biggest players have experimented increase in cases of fraud. Individual fraudsters, as well as organized criminal networks, aim to exploit weak points in the processes by which BNPL loans are applied for and approved – and not just to buy big ticket items. Some scammers target small purchases, such as pizza or alcohol. BNPL’s growing popularity has also attracted bad actors who expect to go undetected in a market flooded with transactions.

Fraud at the BNPL targets many of the same features that make it such a popular option for consumers, such as lenient authentication mechanisms meant to reduce friction for legitimate transactions. Fraudsters create fake accounts to exploit default lines of credit, often making purchases with stolen credit card information. They can even deploy bots to escalate these attacks.

Existing accounts can be even more profitable for bad actors, as a user with a good history can have a much higher credit limit with the BNPL provider. In these cases, fraudsters gain control of accounts through techniques such as credential stuffing, phishing, and SIM card swapping.

Securing BNPL transactions against fraud

One of BNPL’s main vulnerabilities comes from suppliers, who have looser controls in place compared to credit checks associated with banks and credit card companies. This may include a lack of credit checks prior to BNPL approvals. The laddered nature of the method also allows fraudsters to acquire goods for a fraction of the retail price up front, increasing the purchasing power of stolen credit cards used in transactions. During special events or the holiday shopping season, for example, BNPL merchants and suppliers can also reduce security checks to prevent lost sales due to false refusals.

BNPL providers, including Klarna and Afterpay, have worked to outsmart fraudsters by implementing prevention capabilities. According to Afterpay, fraud accounted for less than 1% of its global sales in fiscal 2020, while Klarna said it has protections in place that exceed those offered by credit cards and major banks. Afterpay has attributed its fraud prevention successes to proprietary machine learning (ML) algorithms that adapt as fraudsters search for new entry points.

Tools that would allow faster identify incompatible email addresses and phone numbers could help stem BNPL fraud, as criminals often seek to exploit the lighter identity and credit checks associated with the payment method. Such procedures could be implemented without adding friction to the transaction, as could better verification standards that examine a user’s physical and digital attributes.

Replace reactive solutions with proactive solutions

To keep the positive customer experience that has contributed to BNPL’s popularity while combating fraud requires systems that can react quickly to fraudulent activity. Artificial intelligence (AI)-based fraud prevention tools can respond to threats in real time, identifying fraudulent transactions as they happen. Along with ML-powered methods, these processes can help identify borrowers’ personal documents and stop suspicious activity before money is exchanged.

Suppliers can Choose to partner with companies that specialize in using AI for identity verification and authentication, and merchants can take their own steps to work with ML fraud prevention specialists to spot purchasing activities that follow fraudulent patterns. BNPL moves too quickly for fraud prevention methods such as labeling, rule writing and manual case review, with losses already visible by the time fraud is detected by these methods.

Real-time detection is necessary to effectively stop BNPL fraud. ML can recognize fraud patterns even when they emerge, while automated systems can enable fast, low-friction transactions that are also significantly protected against fraud.

Kayleen C. Rice