Module 1: Introduction to Virtual Card Fraud
🧠 Learning Objectives
By the end of this module, you will be able to:
Explain why virtual card products are especially vulnerable to certain types of fraud
Identify the core fraud vectors affecting card-based payment systems
Understand how attackers think and where they exploit systemic weaknesses
Prepare to map card fraud risks to lifecycle stages
Why Fraud Targets Virtual Cards
Virtual cards are highly programmable, fast to issue, and often backed by prepaid or crypto-derived float. These features enable flexibility for users — but also open up risk surfaces for bad actors.
Characteristics That Attract Fraud:
| Feature | Why It’s Exploitable |
|---|---|
| Instant issuance | Attackers can create many cards quickly and anonymously |
| Prepaid model | Fraudsters can top up and cash out without external bank oversight |
| No 3DS or OTP in some use cases | Easier to test stolen credentials |
| Crypto to fiat top-ups | Adds a pseudo-anonymity layer and FX arbitrage risks |
| Merchant refunds and chargebacks | Used to simulate cash return without goods being returned |
| Weak KYC tier enforcement | Multiple personas and devices go undetected |
Fraud Categories in Virtual Card Infrastructure
| Category | Summary |
|---|---|
| Refund Abuse | Users exploit refund timing, especially to terminated cards |
| Chargeback Fraud | Users dispute legitimate charges after receiving goods/services |
| Merchant Collusion | Fake transactions and refunds processed via known merchants |
| Card Cycling | Users create, spend, and destroy cards to hide patterns |
| Velocity Abuse | High-frequency transactions or rapid card usage designed to exploit system delays |
| Float Laundering | Using top-ups, refunds, and withdrawals across accounts to shift value in untraceable ways |
| MCC Exploitation | Testing transactions against blocked or high-risk merchant categories |
How Real Fraudsters Think
Fraudsters behave like performance marketers or engineers — they test, iterate, and exploit feedback loops.
Typical Mindset:
“How many cards can I create before the system flags me?”
“What merchant categories are not blocked?”
“Can I refund to a terminated card and withdraw?”
“What happens if I use a friend's card to test patterns?”
Fraud is rarely one-time — it’s iterative and adaptive. What worked yesterday won’t work today, and what works today might be blocked tomorrow.
Example Attack Paths
| Path | Description |
|---|---|
| Top-up → Spend → Refund → Terminate | Quick value movement back to float or user wallet |
| Create 5 cards → All fail 3x → Auto-terminate | Stress-testing limits of decline threshold rules |
| Spend from Card A → Refund to Card B | Exploiting missing refund matching validation |
| Withdraw after refund with old CVV | Attempting to bypass withdrawal rules post-failure |
| High refund + chargeback to same MCC | Testing how well you manage merchant risk enforcement |
Prevention Philosophy
Good fraud defense requires:
Visibility: Can you see the pattern across users, cards, and merchants?
Context: Can you match a refund to the original transaction?
Memory: Can you track behavior over time and terminate repeat patterns?
Speed: Can you respond faster than the fraudster adapts?
The system doesn’t need to be perfect — it just needs to make fraud expensive and time-consuming.
Quick Knowledge Check
Why are refunds on terminated cards a red flag?
A. They help users recover unused funds
B. They often have no matching spend and can be exploited for laundering
C. Terminated cards should still process all refunds
D. All refunds are automatically flagged as fraudulent
Correct Answer: C
What is one of the most common velocity fraud patterns?
A. A refund issued 7 days after spend
B. Card usage restricted to single MCC
C. Rapid card creation, top-up, and refund in 10–15 minutes
D. Spending below $10
Correct Answer: C
What should always be validated when issuing a refund?
A. Currency
B. FX spread
C. Existence of a matching spend transaction
D. Cardholder’s email address
Correct Answer: C
