Adaptive Fraud Prevention in B2B Payments: Managing Risk in Credit Card-Based Usage Models

In the rapidly evolving landscape of B2B payments, companies are constantly seeking innovative ways to expand their market reach and streamline operations. The COVID-19 pandemic accelerated this trend, pushing businesses to explore new models and technologies. As growth rates declined for many companies during this period, there was a shift towards more direct-to-consumer (D2C) approaches, leveraging self-serve models and digital onboarding processes. This article explores a novel solution developed to address the complex challenges that arose from this strategic pivot, specifically focusing on credit card-based sign-ups within a usage-based billing model, which added complexity to fraud prevention efforts.

The Challenge

As businesses pivoted from traditional sales force-driven models to self-serve, website-based sign-ups, a new set of challenges emerged. This shift, while necessary for growth in a post-COVID world, exposed companies to increased fraud risks. Many B2B service providers offered usage-based billing models, where customers were invoiced based on their actual utilization rather than a fixed subscription. This meant that upfront billing wasn’t feasible because the final invoice amount depended on each customer’s level of service consumption throughout the month.

This usage-based model created a window of vulnerability where services could be used without immediate payment. New users would sign up using credit cards, utilize services extensively throughout the month, accumulate significant bills, and then fail to pay when invoiced at the end of the month. While this model was essential for flexibility and scalability, it also introduced significant financial risk.

The key challenge was to balance the need for a frictionless onboarding process—crucial for attracting and retaining customers in a competitive digital landscape—with robust fraud prevention measures that could mitigate these risks.

Understanding the Fraud Pattern

The fraud scenarios typically manifested in two distinct ways:

Payment Method Manipulation

The most common pattern involved users who would initially provide valid payment credentials during onboarding but would then manipulate these methods before the billing cycle completed. This manipulation took two primary forms:

• Using virtual cards with limited balances that would be depleted by the time of billing

• Providing regular cards that would subsequently fail at the time of payment

Service Arbitrage

A more sophisticated form of fraud involved users who would:

1. Sign up for the service legitimately using a card

2. Utilize services extensively throughout the month

3. Resell these services to third parties at a markup

4. Evade payment at the end of the billing cycle when their card failed

Since we only allowed customers to sign up using credit cards—due to their ease of use for D2C customers—the vulnerabilities in this payment method became a focal point in our fraud prevention efforts.

Identifying Red Flags Through Behavioral Analysis

One of the most significant insights came from analyzing usage patterns across our customer base. Data analysis revealed clear behavioral thresholds that distinguished legitimate businesses from potential fraudulent actors:

Usage Pattern Analysis

The majority of legitimate customers consistently showed predictable patterns when processing transactions each month. Legitimate customers typically had moderate usage levels early on and scaled gradually over time as they became more familiar with our services. This benchmark became a crucial early warning indicator—when new signups immediately requested unusually high volumes of transactions or utilized services at an enterprise-level scale from day one, it signaled potential fraudulent intent.

Payment Method Validation Evolution

The analysis revealed a critical gap in our initial validation process:

Initial Process:

• Basic card validation

• Number sequence verification

• Card existence confirmation

Critical Finding:

Post-incident analysis showed that a simple $1 authorization hold could have identified many fraudulent payment methods early in the process. Bad actors often used cards that would pass basic validation but fail actual authorization attempts—a distinction that proved crucial for fraud prevention.

Building a Multi-Layered Scoring Model

To address these challenges, we developed a comprehensive weighted average scoring model that incorporated both proprietary behavioral data and third-party validation sources specific to credit card transactions within our usage-based billing system. This model assigned a risk score on a 100-point scale, enabling automated, risk-based decisioning during onboarding.

Data Integration Points

• Payment processor reputation data (e.g., Stripe Radar)

• Email verification systems to detect temporary or suspicious email addresses

• Historical usage patterns based on service consumption levels

• Card authorization checks

• Real-time behavioral analysis

Risk-Based Access Tiers

The model segmented customers into three distinct risk tiers based on their behavior during onboarding:

Validation and Performance Metrics

Back-testing analysis of the scoring model revealed compelling evidence of its effectiveness in identifying and managing risk while maintaining business growth—specifically within this credit card-based usage model. The data demonstrated that the risk-tiering approach successfully balanced fraud prevention with customer acceptance.

Medium Risk Tier Success

The approach particularly validated our handling of medium-risk customers:

• Limiting initial access while allowing customers to build payment history proved effective.

• The majority of medium-risk customers successfully paid their invoices after establishing trust through their behavior over time.

These metrics confirmed that limiting access initially but allowing legitimate customers to expand their service consumption after proving reliability was an effective strategy for balancing risk management with business growth.

Dynamic Model Evolution

The scoring model was designed to be adaptive, incorporating continuous learning from historical data analysis specific to usage-driven transactions. This dynamic approach allowed for regular refinement of risk thresholds based on accumulated data and changing fraud patterns.

Threshold Optimization

Rather than maintaining static thresholds, the model underwent periodic calibration:

• Historical back-testing informed threshold adjustments.

• Risk tier boundaries were reassessed based on emerging fraud patterns.

• Scoring weights were fine-tuned as new data became available.

• Performance metrics guided further modifications.

This iterative approach ensured that:

• Risk thresholds remained relevant to current market conditions.

• The model adapted to emerging fraud patterns.

• Customer acceptance rates were optimized.

• False positives were minimized.

Best Practices for Implementing a Scoring Model for Fraud Prevention

When developing a fraud prevention strategy in B2B payments—particularly where credit cards and usage-based billing models are involved—it’s essential to balance risk management with customer experience. Based on lessons learned from building this multi-layered scoring model, here are some best practices for businesses looking to implement similar systems:

1. Leverage Behavioral Data Early

• Why it matters: Behavioral data collected during onboarding (e.g., transaction volume, speed of sign-up, and usage patterns) can provide early indicators of potential fraud. Monitoring these behaviors in real-time allows businesses to detect anomalies that traditional credit checks might miss.

• Best Practice: Set clear thresholds for normal behavior based on historical data. For example, if legitimate customers typically process moderate transaction volumes early on in usage models, flag any significant deviations as potential fraud risks.

2. Incorporate Third-Party Data Sources

• Why it matters: Third-party data sources like payment processor reputation tools (e.g., Stripe Radar) and email verification systems can provide additional layers of validation. These tools help identify fraudulent actors using temporary emails or virtual credit cards.

• Best Practice: Integrate multiple third-party services to cross-check customer information during onboarding. This not only strengthens fraud detection but also ensures compliance with regulatory standards like Know Your Customer (KYC) and Anti-Money Laundering (AML) requirements.

3. Adopt a Tiered Risk-Based Approach

• Why it matters: Not all customers pose the same level of risk. A tiered approach allows businesses to grant different levels of access based on each customer’s risk profile. This minimizes exposure to high-risk customers while still allowing legitimate users to onboard smoothly.

• Best Practice: Use a scoring model that segments customers into risk tiers (e.g., low, medium, high). For medium-risk customers, limit initial access (such as capping transaction volumes) until they demonstrate reliable payment behavior.

4. Continuously Refine Through Back-Testing

• Why it matters: Fraud patterns evolve over time, so your scoring model should be adaptive. Regular back-testing helps refine risk thresholds and scoring weights based on new data.

• Best Practice: Periodically review past cases of fraud and non-payment to identify any missed red flags. Adjust your scoring model accordingly to stay ahead of emerging fraud tactics.

5. Balance Automation with Human Oversight

• Why it matters: While automation is key for scaling fraud prevention efforts, certain high-risk cases may require manual intervention. Automated systems can flag potential fraud, but human review adds an extra layer of scrutiny.

• Best Practice: Implement automated scoring for most customers but include manual reviews for those flagged as high risk or falling below certain thresholds. This ensures that legitimate customers aren’t unfairly blocked while minimizing exposure to bad actors.