Cutting-Edge Verification Can Curb Buy Now Pay Later Fraud

The ongoing threat of buy now, pay later (BNPL) fraud puts organizations in the position of strengthening their defenses while maintaining the speed and convenience customers expect.

BNPL is booming. ACI reported BPNL grew 237% in the first half of 2024 compared with the same period in 2023. But the payment method’s rapid growth makes it a target for fraudsters.

Potential BNPL Fraud Threats

Fraudsters can use many methods to attack BNPL platforms.

Synthetic Identity Fraud
Synthetic identity fraud involves creating an identity by combining fake information with actual data. For example, a fraudster could pair a real Social Security number with a fake address and other synthetic data points.

Merchant Fraud
Merchant fraud involves fake merchant accounts that appear as legitimate businesses but are just fronts for various fraud schemes.

Account Takeover Fraud
This attack involves taking control of an account to access funds, perform unauthorized transactions or gain entry to other accounts.

New Account Fraud

New account fraud can initially be cloaked in benign activities, lulling an organization into assuming it onboarded a legitimate customer. A fraudster uses a stolen, false or synthetic identity to pass standard verification processes and open a new BNPL account. The fraudster can perform multiple transactions before being detected because the first payments are often a month away.

BNPL Fraud Prevention Strategies

Organizations can defend against BNPL fraud with cutting edge data, document and biometric verification technology.

They can apply those verification techniques through a risk-based approach to onboarding that automatically adjusts identity checks based on various signals, such as location, account history, transaction type and amount. That approach places greater scrutiny on high-risk accounts while allowing good customers  move quickly through onboarding.

BNPL platforms can use a broad range of methods to form a risk profile during onboarding and beyond.

• Email Signals – How old is the customer’s email address? How often is it used? 
• Phone Signals – Similarly, a phone number’s history, use and ownership provide information about who is behind the account.
• Network Signals – The IP address provides another layer of insight. Does it match the expected information of the user? Is it blocked, spoofed or involved in fraudulent activity?
• Pattern-Based Signals – Information from email, phone and IP addresses, along with velocity and risk scores, can be cross-referenced and analyzed for patterns.

BNPL platforms have numerous other fraud prevention measures at their disposal.

• Payment Fraud Analytics – Payment fraud analytics applies AI to large data sets to fuel robust identity checks, such as through biometrics that detect presentation attacks and spoofing attempts
• Merchant Verification – BNPL platforms can conduct enhanced due diligence to help ensure they partner with reputable merchants
• Fraud Detection Systems – Machine learning algorithms can monitor and flag unusual patterns or behaviors in real time to help detect fraudulent activity
• Identity Data and Credit Checks – Soft credit checks or alternative data sources can help verify customers without causing unnecessary friction
• Transaction Limits and Controls – Transaction thresholds can trigger additional verification to limit risk for high-value purchases
• Continuous Monitoring and Behavioral Analytics – Transaction monitoring can help identify patterns indicative of fraud, such as multiple BNPL accounts or irregular purchasing behaviors

A Holistic View of Fraud and Risk

Fraud prevention is just one piece of the puzzle for BNPL platforms that have to navigate payments compliance requirements in markets across the globe.

Organizations can complete the puzzle by breaking down onboarding silos and blending Know Your Customer, Know Your Business, Anti-Money Laundering and fraud prevention services through a single global platform.