Buy now pay later (BNPL) is a flexible, affordable way for consumers to shop online. These no-interest low-credit schemes are popular among e-commerce retailers too, who recognize the high conversion rates that BNPL can offer.
As appealing as buy now, pay later is, there are rising risks associated with this type of financing. BNPL’s unique structure and the demand for a quick and frictionless transaction experience contribute to the optimal environment for fraudsters. BNPL fraud risk is high—and rising.
Strong BNPL risk management starts with a foundational understanding of current buy now, pay later scams. Let’s examine the growing risks associated with offering BNPL services and some proactive measures you can take to optimize your BNPL risk management.
What to know about BNPL fraud risk
The risk presented by buy now, pay later schemes is more challenging than other types of payment fraud. “Fraud on BNPL platforms differs from general e-commerce fraud because of the unique way BNPL is structured,” explained Fortune. “With payments spread across four (or more) transactions, fraud actors have an expanded “attack surface,” giving them more opportunities for infiltration.”
Complicating matters further are three different buy now, pay later operating models—each of which comes with its own risk for fraud. Synthetic identity fraud, account takeover fraud, and chargeback fraud are among the most common buy now pay later risks businesses take when offering this payment option.
“Criminal gangs are exploiting weaknesses in the application process for BNPL loans…using clever tactics to slip through undetected and steal items ranging from pizza and booze to video game consoles,” reported CNBC.
This service is also at risk because many companies in the industry don’t conduct formal credit checks. Instead, they rely on internal algorithms to determine someone’s creditworthiness—meaning they work with limited information about the consumer.
BNPL is projected to only get more popular, which is why businesses need to be proactive now about BNPL risk management.
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How to assess and quantify BNPL fraud risk
Buy now, pay later scams are evolving quickly. Assessing BNPL fraud risk should be a regular, ongoing activity involving the right tools to scan and monitor for signs of scams. Here’s how to assess and quantify BNPL fraud risk.
1. Identify risk factors
There are two types of buy now pay later risks to consider when offering this payment option. The first is credit risk: when someone wants to repay their BNPL loan but isn’t able to (for financial or other reasons). Mitigating this risk involves identifying customer characteristics such as their age, location, and credit history. You may also choose to gather account characteristics, such as new accounts, to understand the level of credit risk associated with your BNPL users.
The second, more difficult BNPL risk is fraud risk. This refers to customers who are attempting to purchase goods or services using stolen identities or credit cards with no intention of making repayments. For this risk, transaction characteristics, such as high-value items, unusual purchase patterns, and shipping addresses inconsistent with billing addresses, can be indicators of fraud.
2. Quantify the buy now pay later risks
To gauge the risk your organization faces when providing Buy Now, Pay Later (BNPL) services, it’s essential to consider the probability of different types of fraud, evaluate their financial implications, and ultimately generate a comprehensive risk score.
Start by determining the probability of account takeover fraud, synthetic identity fraud, and other BNPL scams occurring based on historical data, industry trends, and risk factors unique to your business.
Then, assess the potential hard and soft costs to your business. Hard costs include immediate expenses, such as the cost of chargebacks. Soft costs include less quantifiable things, like damage to your brand reputation. Try to assign a cost to those less tangible things.
Finally, multiply the likelihood by the impact to calculate a risk score for each type of fraud. This score can help prioritize your efforts and allocate resources accordingly.
3. Implement the right tools and mitigation strategies
Armed with this information, your organization can move forward to proactively manage BNPL risks. In the next section, we’ll cover some of the key tools and best practices organizations use to mitigate BNPL fraud risk.
BNPL tools and best practices
With so many different forms of fraud emerging around BNPL, it is crucial to use a multi-layered approach to combat it. Start by implementing these best practices and tools to protect your business.
BNPL risk management: best practices
A good first step is to review shoppers’ credentials while finding a balance between security and a frictionless customer experience. Policies to support people who struggle with making their installment payments on time can reduce bad debt and even first-party fraud. Review requirements shoppers have to meet to qualify for the BNPL option.
Merchants can also mitigate chargeback fraud by updating their return policies and making it difficult for fraudsters to implement chargebacks. Train members of your team to identify fraudulent activity and educate shoppers about BNPL fraud risks.
In addition, financial institutions, in particular, can implement strong Customer Due Diligence (CDD) and Know Your Customer (KYC) procedures. Use multi-layered identity verification methods like document verification, facial recognition, and data validation to ensure customer authenticity.
The most powerful way to avoid buy now, pay later scams is through utilizing strong fraud detection technology.
BNPL risk management: best tools
There are three things to look for in assessing BNPL risk management technologies. The first is to find one that uses machine learning and AI. These technologies can analyze vast amounts of data to identify patterns and predict fraudulent activity.
Fraud.net’s Application AI uses artificial intelligence to verify legitimate customers and vendors in real-time while stopping fraudulent ones before they can cause further harm. Likewise, our Transaction AI tool offers real-time, continuous fraud and AML transaction monitoring.
By collating your existing data and supplementing it with relevant external BNPL data, Application AI provides insights to spot fraudulent behavior based on prior transactions. It analyzes vital identity factors during account creation, cross-verifies critical identity data, and flags potential high-risk behavior rapidly enough that the customer’s experience remains seamless.
Combined with Transaction AI, these tools provide a powerful defense against would-be scammers. Transaction AI’s identity proofing, verification, and customer behavior analysis ensure that a user is who they claim to be. Plus, Transaction AI’s 3DS requires the card issuer to verify the identity of the shopper at checkout. Link analysis can detect connections to widespread schemes.
BNPL isn’t going anywhere anytime soon. Merchants who plan ahead and implement BNPL risk management will be at a considerable advantage as this payment method—and its scams—continues to grow.
Learn more about Fraud.net’s solutions to BNPL scams. Request a demo today.