To protect your organization against synthetic identity fraud, you need advanced technologies and multiple tools working together.
Buy-now-pay-later (BNPL) is one of the fastest-growing payment options among customers, especially after the surge in e-commerce spurred by the COVID-19 pandemic. A convenient way to purchase items even with low spending power, BNPL is a “loan” offered at the point of sale, allowing customers to buy what they want now and pay for it in installments over time. Unfortunately, this convenience is not without risk, namely synthetic identity fraud.
A range of payment options
BNPL programs offer various payment options that provide a great deal of flexibility and freedom to consumers. These options can be broadly divided into three types of payment structures:
- Split Pay, where the payment is split into installments paid over a period of time. For example, $200 can be paid over four weeks in equal $50/week installments.
- Pay Later, where consumers delay payment of the total amount. For example, a consumer could buy a product costing $100 now, paying $10 immediately and the remaining $90 in a month.
- Longer-term financing, where BNPL providers finance a loan at either a 0% interest rate or a certain amount of interest or fee over an extended period, like 12 months. Customers often use this method for more expensive purchases. For example, Affirm, a BNPL provider in the US, has financed Peloton bikes for up to 39 months at 0% interest.
Customers can purchase even expensive products with these options while shouldering less financial burden. Furthermore, merchants benefit from reduced cart abandonment and higher conversion rates.
BNPL’s rapid growth in popularity
Due to the benefits BNPL offers both consumers and merchants, it is gaining popularity across the globe. In fact, McKinsey found that 60% of consumers in the US are likely to use it over the next 6-12 months. This growing popularity has resulted in interest from several providers, with everyone from fintechs like PayPal to more traditional financial institutions like Mastercard entering the BNPL market.
The risk BNPL presents
Unfortunately, the rapid adoption of this new shiny scheme comes with challenges. BNPL’s unique structure and the demand for a quick and frictionless transaction experience create favorable circumstances for fraud. The rapid nature of the point-of-sale offering leaves no time for the thorough credit checks otherwise required for loans. This makes BNPL an easy entry for fraudsters. More specifically, synthetic identity fraud has a hybrid nature that is difficult to detect within BNPL options.
Synthetic identity fraud
One of the more complex forms of identity theft, synthetic identity fraud (SIF) uses a combination of real and false personal identifying information to create a new identity. Fraudsters usually use a legitimate Social Security number and pair it with false details for the name, address, and date of birth. Effective SIF fraudsters go one step further and cultivate good credit for the false identity before defaulting on large payments.
This multifaceted approach makes SIF more challenging for organizations and law enforcement. According to a Federal Reserve analysis, SIF schemes can masquerade as “good” consumer behavior, with 70% of suspected cases temporarily exhibiting typical consumer patterns. So, when the fraudsters inevitably default on their payments, the fraud is written off as “bad debt.” Unfortunately, this leaves SIF cases largely unreported.
What makes it worse is that the hybrid identity makes it nearly impossible for organizations to track down the fraudster once they abandon the identity. Combined with the already low threshold for fraud that BNPL presents, synthetic identity fraud is a significant problem for BNPL providers.
Preventing synthetic identity fraud in BNPL programs
Since SIF is complex and challenging to detect. As a result, it requires more than the traditional identity verification methods like Know Your Customer or the Customer Identification Program to combat it. To protect your organization against synthetic identity fraud, you need advanced technologies and multiple tools working together. Integrating AI tools into your fraud prevention solution is the only way to prevent sophisticated SIF attacks.
Fortunately, Fraud.net’s Application AI tool can shield you from synthetic identity fraud. The tool helps you instantly verify genuine customers and identify potentially fraudulent applications, including those generated using synthetic identities. It also provides real-time risk assessments of inbound BNPL applications, which your fraud teams can quickly validate to identify the fraudulent applications.
Application AI for Identity Verification
Application AI uses a highly effective approach to combat synthetic identity fraud in BNPL transactions:
- By collating your existing data and supplementing it with relevant external BNPL data, it provides insights to spot fraudulent behavior based on prior transactions.
- With improved account authentication, it analyzes vital identity factors during account creation, cross-verifies critical identity data, and flags potential high-risk behavior, all rapidly enough that the customer’s experience remains seamless.
- Application AI’s custom machine learning models automatically adapt to new SIF and BNPL-related schemes and variants.
- Dynamic device fingerprinting helps it capture the device ID used for BNPL applications and analyzes user behavior and other information to create a unique digital fingerprint, helping further protect against even hybrid synthetic fraud.
Want to find out what Application AI can do for your business? Get in touch today to book a free, no-strings-attached demo with us!