Fraud Prevention
Last Updated: 2025-02-17 Status: Complete
Payment fraud costs the industry billions annually. For PayFac platforms, effective fraud prevention is essential—not just to protect revenue, but to avoid network monitoring programs and maintain processing relationships.
Quick Reference
| Metric | 2024 Data | Trend |
|---|---|---|
| Global Card Fraud | $33.4 billion | Growing to $43B by 2026 |
| CNP Fraud Share | 65% of fraud losses | Increasing |
| Friendly Fraud | Up to 75% of chargebacks | Growing 40% by 2026 |
| CNP vs CP Fraud Rate | 15.5x higher | Persistent gap |
Fraud Landscape Overview
Fraud Categories
By Source
| Category | Description | % of Fraud | Prevention Difficulty |
|---|---|---|---|
| Third-Party | Stolen cards, credentials | 60-70% | Medium |
| First-Party | Friendly fraud, disputes | 36%+ (growing) | Very High |
| Synthetic | Fabricated identities | Growing | High |
By Transaction Type
| Type | Fraud Rate | vs. Card-Present |
|---|---|---|
| Card-Present (CP) | 0.06% | Baseline |
| Card-Not-Present (CNP) | 0.93% | 15.5x higher |
CNP Fraud Dominance
CNP fraud accounts for 65% of total fraud losses despite being a smaller share of transaction volume. E-commerce and digital payments are primary targets.
Defense Layers
Effective fraud prevention uses multiple layers:
Layer Effectiveness
| Layer | Detection Rate | False Positives | Implementation |
|---|---|---|---|
| AVS/CVV | 20-30% | Low | Easy |
| 3D Secure | 70-80% | Low | Medium |
| Device Intelligence | 40-50% | Medium | Medium |
| ML Scoring | 70-90% | Low | Complex |
| Combined | 90-95% | Optimized | - |
Section Contents
Fraud Patterns
- Card testing attacks and detection
- Friendly fraud (first-party fraud)
- Account takeover (ATO)
- CNP fraud trends
Detection Tools
- AVS and CVV verification
- Device fingerprinting
- Machine learning fraud scoring
- Rules-based detection
3D Secure
- 3DS2 implementation and flows
- Liability shift rules
- SCA/PSD2 compliance
- Frictionless vs. challenge authentication
Quiz
- Self-assessment questions
Key Statistics (2024-2026)
Fraud Volume
| Metric | 2024 | 2026 Projected |
|---|---|---|
| Global Card Fraud | $33.4B | $43B |
| US CNP Fraud | $9.2B | $12.9B |
| Global Chargebacks | 238M | 337M (42% increase) |
Fraud by Type
| Fraud Type | % of Total | Trend |
|---|---|---|
| Friendly Fraud | Up to 75% of chargebacks | +40% by 2026 |
| First-Party Fraud | 36% of all fraud | Up from 15% (2023) |
| Account Takeover | 52% of loyalty fraud | Growing |
Detection Performance
| Method | Detection Rate | Notes |
|---|---|---|
| 3DS2 Frictionless | 90-95% pass | Most transactions avoid challenge |
| ML Fraud Scoring | 95% recall | Top systems achieve 97% AUC |
| Device + Behavioral | 90%+ | Combined approach best |
PayFac Fraud Responsibilities
As a PayFac, you have specific fraud obligations:
PayFac Fraud Obligations
| Obligation | Requirement |
|---|---|
| Sub-merchant monitoring | Real-time fraud monitoring per merchant |
| Ratio tracking | Monitor fraud ratios against network thresholds |
| 3DS implementation | Offer 3DS to sub-merchants |
| High-risk identification | Flag merchants with elevated fraud |
| Network compliance | Stay below VAMP/ECP thresholds |
Related Topics
- Network Monitoring Programs - VAMP, ECP, MATCH consequences
- Chargeback Management - Handling fraud chargebacks
- PCI Compliance - Protecting cardholder data
Onboarding Context:
- Risk Factors - Fraud indicators identified during underwriting
- Ongoing Monitoring - Continuous fraud monitoring post-approval