Module 7: Designing Controls & Prevention Systems
Objective
This module equips your engineering, compliance, and product teams to move from fraud detection to prevention by embedding rule-based and intelligent safeguards throughout your virtual card infrastructure.
Key goals:
Reduce exposure to known attack vectors
Shift fraud from reactive to proactive handling
Block high-risk actions at the edge
Minimize false positives without weakening controls
Security by Design
Designing fraud-resistant infrastructure requires a multi-layered approach across:
| Layer | Strategy |
|---|---|
| API | Enforce rate limits, authentication, payload verification |
| Card Lifecycle | Restrict velocity of creation, termination, and top-up |
| Transaction Controls | Use MCC, BIN, geolocation, and spend behavior |
| Refund System | Require strict matching, validation, and time checks |
| Identity | Leverage device, IP, biometric, and behavioral signals |
Reminder: Prevention starts with design. Controls must be active before fraud occurs, not after.
Rule-Based Controls That Work
Controls to Implement Immediately:
| Category | Rule |
|---|---|
| Card Creation | Max 2 cards per user per 24 hours |
| Top-up | Only allowed if card is active |
| Spend | Deny if MCC ∈ [6051, 4829, 7995] or BIN conflict |
| Refund | Require matching spendRef within past 30 days |
| Chargebacks | Auto-flag user if > 2 disputes in 7 days |
| Declines | Freeze card if 3 consecutive failed authorizations |
| Withdrawals | Require $1 minimum post-withdrawal balance |
Behavior-Based Prevention (Smart Systems)
Complement rule-based checks with behavioral systems:
| Input | Action |
|---|---|
| Device fingerprint seen in past fraud | Prevent card creation, block login |
| User's velocity exceeds norm | Limit top-ups or spend until review |
| User spends at new MCC + new merchant | Soft-approve with secondary verification |
| Pattern matches refund laundering graph | Trigger full manual review |
| User moves float across card-wallet-card loop | Lock and route to treasury team |
Insight: Intelligent risk engines reduce false positives while catching high-complexity fraud early.
Real-Time Transaction Decision Layer
For every card transaction, design a decision layer:
This enables:
Transparency in enforcement
Logging and auditing of risk decisions
Fast debugging of false positives
Data for tuning fraud rules
Webhook & Log-Driven Defense
Webhooks can serve not just as notifications, but detection points:
| Webhook | Risk Action |
|---|---|
| transaction.reversed without transaction.debit | Investigate — reversal abuse |
| refund without matching authorization | Freeze and hold until review |
| transaction.declined + card terminated | User-triggered attack pattern |
| Multiple topup.failed on same card | Float abuse or wallet manipulation attempt |
All webhooks must be:
Authenticated
Timestamped
Logged to a fraud log channel or monitoring system
Mapped to float movements
User Lifecycle Protections
A user's full behavior must be scored across:
Identity (KYC, IP, phone number, email)
Devices used
Card usage
Refund or dispute rate
Card creation/termination frequency
Wallet movement history
Design a risk score across these vectors to:
Approve actions (soft vs hard)
Limit daily activity
Escalate to human review
Block suspicious behavior entirely
Policy Enforcement Examples
| Policy | Implementation |
|---|---|
| Refunds only go to active cards | Check card status before credit |
| Chargebacks above threshold block card creation | Store rolling 7-day chargeback counter |
| Declines over threshold freeze card | Count declines in the last 15 minutes |
| High refund velocity across users to same MCC | Block merchant temporarily |
| IP shared by flagged users | Add to firewall blocklist |
Module 7 Knowledge Check
1. Why should refund logic enforce a matching spend reference?
A. To prevent reversal loops
B. To speed up refunds
C. To reduce MCC fees
D. To confirm legitimacy and block fake refunds
Answer: D
2. What's a risk of allowing refund-to-float for terminated cards without review?
A. Poor UX
B. Lost merchant support
C. Float laundering and user abuse
D. Extra audit cost
Answer: C
3. What is the benefit of mapping all decisions in real time?
A. Allows better merchant reconciliation
B. Helps spot webhook issues
C. Makes logs easier to sort
D. Provides transparency and auditability of fraud logic
Answer: D
